Blood biomarkers for suicidality

ABSTRACT

Biomarkers and methods for screening expression levels of the biomarkers for predicting and tracking suicidality, as well as for monitoring response to a treatment for suicidal risk and for determining suicidal risk as a side-effect of an antidepressant are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser. No. 14/194,024 filed on Feb. 28, 2014, which claims priority to U.S. Provisional Patent Application No. 61/770,696 filed on Feb. 28, 2013, both of which are hereby incorporated by reference in their entireties.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under OD007363 awarded by the National Institutes of Health. The Government has certain rights in the invention.

BACKGROUND OF THE DISCLOSURE

The present disclosure relates generally to blood biomarkers and their use for predicting mental state, and in particular, for predicting a subjects' risk of suicide (also referred to herein as “suicidality”). More particularly, the present disclosure relates to gene expression biomarkers, and to methods of screening for biomarkers, for identifying subjects who are at risk of committing suicide and methods for monitoring response to potential treatments by analyzing biomarkers.

Suicides are a leading cause of death in psychiatric patients, and in society at large. Particularly, suicide accounts for one million deaths worldwide each year. There are currently no objective tools to asses and track changes in suicidal risk without asking the subjects directly. Such tools, however, could prove substantially advantageous as the subjects at risk often choose not to share their suicidal ideation or intent with others, for fear of stigma, hospitalization, or that, in fact, their plans will be thwarted.

Conventionally, a convergence of methods assessing the subject's internal subjective feelings and thoughts, along with external, more objective, ratings of actions and behaviors, are used de facto in clinical practice, albeit not in a formalized and systematic way. Accordingly, there exists a need to develop more quantitative and objective ways for predicting and tracking suicidal states. More particularly, it would be advantageous if objective screening methods could be developed for determining expression levels of biomarkers to allow for determining suicidal risk and other psychotic depressed mood states, as well as monitoring a subject's response to treatments for lessening suicidal risk.

SUMMARY OF THE DISCLOSURE

The present disclosure relates generally to predicting and tracking suicidality. Particularly, the present disclosure is directed to screening expression levels of biomarkers for predicting and tracking suicidality, and other psychotic depressed mood states, as well as for monitoring response to a treatment for suicidal risk. In one embodiment, the screening methods are useful in determining the suicidal risk of antidepressant treatment in a subject, which has been shown to be rare, but very serious in certain situations.

Biomarkers useful for identifying subjects at risk for suicide, as well as useful for monitoring the risk of suicide following treatment have been discovered. Accordingly, the present disclosure is directed to methods of identifying a subject at risk for suicide. The present disclosure is further directed to methods for monitoring response of a subject at risk for suicide to a treatment for suicide risk.

By monitoring and tracking changes in suicide state, the present disclosure allows for detection of an increased suicide risk prior to any suicide attempt by a subject, and further allows subjects at risk of suicide and other psychotic depressed mood states to be monitored and treated effectively. Accordingly, in another embodiment, the present disclosure relates to predicting future hospitalization for subjects being at risk for suicide and other psychotic depressed mood states such to provide sufficient monitoring and treatment to the subjects.

In one aspect, the present disclosure is directed to a method for identifying a subject at risk for suicide. The method includes obtaining a reference expression level of a blood biomarker; and determining an expression level of the blood biomarker in a sample obtained from the subject, wherein a change in the expression level of the blood biomarker in the sample obtained from the subject as compared to the reference expression level indicates a risk for suicide.

In another aspect, the present disclosure is directed to a method for monitoring response of a subject to a treatment for suicidal risk. The method includes obtaining an expression level of a biomarker from the subject; administering a treatment for suicidal risk to the subject; and determining an expression level of the biomarker in a sample obtained from the subject after the treatment is administered, wherein a change in the expression level of the biomarker in the sample obtained from the subject after the treatment is administered as compared to the expression level before administration indicates a response to the treatment.

In another aspect, the present disclosure is directed to a method for determining suicidal risk of an antidepressant, the method comprising: obtaining an expression level of a biomarker from a subject; administering an antidepressant to the subject; and determining an expression level of the biomarker in a sample obtained from the subject after the antidepressant is administered, wherein a change in the expression level of the biomarker in the sample obtained from the subject after the antidepressant is administered as compared to the expression level of the biomarker before the antidepressant is administered indicates a suicidal risk.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be better understood, and features, aspects and advantages other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such detailed description makes reference to the following drawings, wherein:

FIG. 1A depicts the Discovery Cohort intra-subject and inter-subject analyses as discussed in Example 1.

FIG. 1B depicts the HAMD17 Suicidal Ideation scores as discussed in Example 1.

FIG. 2 depicts the convergent functional genomics (CFG) approach for identification and prioritization of genomic biomarkers for suicidality as discussed in Example 1.

FIGS. 3A-I depict the validation of biomarkers in the Validation Cohort (i.e., suicide completers) as discussed in Example 1.

FIGS. 4A-F depict SAT1 expression in the bipolar discovery cohort: relationship with suicidal ideation, mood, psychosis, anxiety, and stress as discussed in Example 1.

FIGS. 5A-E depict SAT1 expression levels versus subsequent hospitalizations due to suicidality as analyzed in Example 2.

FIGS. 6A-E depict SAT1 expression levels versus prediction of future hospitalizations due to suicidality as analyzed in Example 2.

FIGS. 7A-C depict expression levels of PTEN and MAP3K3 versus prediction of future hospitalizations due to suicidality as analyzed in Example 2.

FIGS. 8A-8C depict multi-dimensional prediction of future psychiatric hospitalizations due to suicidality as analyzed in Example 2. Data in each dimension was normalized to a 0-100 scale (with the mood VAS scale inverted, as the assumption was made that depressed mood states would more closely correlate with suicidality). The angle between dimensions was assumed to be 90 degrees, and a simple Pythagorean distance from origin score was calculated. The distribution of this score in the test cohort was used to generate an ROC curve for hospitalizations due to suicidality. FIG. 8A). ROC curve. FIG. 8B). Detailed results. FIG. 8C). 3 D visualization.

FIGS. 9A and 9B depict multi-dimensional prediction of future psychiatric hospitalizations due to suicidality as analyzed in Example 2. Data in each dimension was normalized to a 0-100 scale (with the mood VAS scale inverted, as the assumption was made that depressed mood states would more closely correlate with suicidality). The angle between dimensions was assumed to be 90 degrees, and a simple Pythagorean distance from origin score was calculated. The distribution of this score in the test cohort was used to generate an ROC curve for hospitalizations due to suicidality. FIG. 9A). ROC curve. FIG. 9B). Detailed results.

FIG. 10 depicts the clinical measures used in the multi-modal approach in FIGS. 8A-8C.

While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are herein described below in detail. It should be understood, however, that the description of specific embodiments is not intended to limit the disclosure to cover all modifications, equivalents and alternatives falling within the spirit and scope of the disclosure as defined by the appended claims.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure belongs. Although any methods and materials similar to or equivalent to those described herein may be used in the practice or testing of the present disclosure, the preferred materials and methods are described below.

In accordance with the present disclosure, biomarkers useful for objectively identifying subjects at risk for suicide, as well as for monitoring the risk of suicide following treatment and determining the risk of suicide following administration of antidepressants have been discovered. In one aspect, the present disclosure is directed to a method for identifying a subject at risk for suicide. The method includes obtaining a reference expression level of a blood biomarker; and determining an expression level of the blood biomarker in a sample obtained from the subject. A change in the expression level of the blood biomarker in the sample obtained from the subject as compared to the reference expression level indicates a risk for suicide. In some embodiments, the methods further include obtaining clinical risk factor information and clinical scale data such as for anxiety, mood and/or psychosis from the subject in addition to obtaining blood biomarker expression level in a sample obtained from the subject. This combined clinical data and blood biomarker expression level can further improve predictability of the risk of suicide as shown in FIGS. 8A-8C and 9A-9B.

As used herein, “a subject at risk for suicide” refers to a subject diagnosed by one skilled in the art such as, for example, a clinician, using established protocols and methods for diagnosing suicidality. Such methods can include, for example, rigorous clinical interview using clinical standards for assessing and diagnosing whether a subject is at risk for suicide. Suicidality diagnosis can be established using, for example, questionnaires to identify suicidal ideation. Diagnosis can include diagnostic assessment using psychiatric rating scales including, for example, the Hamilton Rating Scale for Depression (HAMD-17), which includes a suicidal ideation rating item, Beck Scale for suicide ideation, Columbia Suicide Severity Rating Scale, The Kessler Psychological Distress Scale, and combinations thereof.

Particularly suitable subjects are humans. Suitable subjects can also be experimental animals such as, for example, monkeys and rodents, that display a behavioral phenotype associated with suicide, for example, a mood disorder or psychosis.

As used herein, “a reference expression level of a biomarker” refers to the expression level of a biomarker established for a subject with no suicidal ideation, expression level of a biomarker in a normal/healthy subject with no suicidal ideation as determined by one skilled in the art using established methods as described herein, and/or a known expression level of a biomarker obtained from literature. As known by those skilled in the art, “suicidal ideation” refers to thoughts, feelings, intent, external actions and behaviors about completing suicide. Suicidal ideation can vary from fleeting thoughts to unsuccessful attempts.

As used herein, “expression level of a biomarker” refers to the process by which a gene product is synthesized from a gene encoding the biomarker as known by those skilled in the art. The gene product can be, for example, RNA (ribonucleic acid) and protein. Expression level can be quantitatively measured by methods known by those skilled in the art such as, for example, northern blotting, amplification, polymerase chain reaction, microarray analysis, tag-based technologies (e.g., serial analysis of gene expression and next generation sequencing such as whole transcriptome shotgun sequencing or RNA-Seq), Western blotting, and combinations thereof.

Suitable biomarkers found to have a change in expression level include, for example, spermidine/spermine N1-acetyltransferase 1 (SAT1); forkhead box N3 (FOXN3); guanylate binding protein 1 (GBP1); phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5); apolipoprotein L2 (APOL2); ATPase H+ transporting lysosomal 9 kDa, V0 subunit e1 (ATP6V0E1); GRINL1A complex locus (GCOM1); interleukin 1 beta (IL1B); lipoma HMGIC fusion partner (LHFP); lipase A (LIPA); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); phosphatase and tensin homolog (PTEN); reversion-inducing-cysteine-rich protein with kazal motifs (RECK); tumor necrosis factor (ligand) superfamily member 10 (TNFSF10); ATP-binding cassette, subfamily A (ABC1) member 1 (ABCA1); Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357); cancer susceptibility candidate 1 (CASC1); dehydrogenase/reductase (SDR family) member 9 (DHRS9); disrupted in schizophrenia 1 (DISC1); eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2); uncharacterized LOC727820 (LOC727820); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6); RNA binding motif protein 47 (RBM47); RPTOR independent companion of MTOR complex 2 (RICTOR); sterile alpha motif domain containing 9-like (SAMD9L); scavenger receptor class F member 1 (SCARF1); solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36A1); signal transducer and activator of transcription 1, 91 kDa (STAT1); cytochrome c oxidase subunit Vb (COX5B); SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCA1); ubiquitin-like modifier activating enzyme 6 (UBA6); zinc finger CCCH-type antiviral 1 (ZC3HAV1), CD24, ATP13A2, EPHX1, HTRA1, SPTBN1, MBNL2, OR2J3, RHEB, DBP, and combination thereof. Particularly suitable biomarkers include SAT1, MARCKS, PTEN, MAP3K3, and combinations thereof.

As used herein, a “change” in the expression level of the biomarker refers to an increase or a decrease of by about 1.2-fold or greater in the expression level of the biomarker as determined in a sample obtained from the subject as compared to the reference expression level of the biomarker. In one embodiment, the change in expression level is an increase or decrease by about 1.2 fold.

In one embodiment, the expression level of the blood biomarker in the sample obtained from the subject is increased as compared to the reference expression level of the biomarker. It has been found that an increase in the expression level of particular blood biomarkers in the sample obtained from the subject as compared to the reference expression level of the biomarker indicates a risk for suicide. Suitable biomarkers that indicate a risk for suicide when the expression level increases can be, for example, spermidine/spermine N1-acetyltransferase 1 (SAT1); forkhead box N3 (FOXN3); guanylate binding protein 1 (GBP1); phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5); apolipoprotein L2 (APOL2); ATPase H+ transporting lysosomal 9 kDa, V0 subunit e1 (ATP6V0E1); GRINL1A complex locus (GCOM1); interleukin 1 beta (IL1B); lipoma HMGIC fusion partner (LHFP); lipase A (LIPA); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); phosphatase and tensin homolog (PTEN); reversion-inducing-cysteine-rich protein with kazal motifs (RECK); tumor necrosis factor (ligand) superfamily member 10 (TNFSF10); ATP-binding cassette, subfamily A (ABC1) member 1 (ABCA1); Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357); cancer susceptibility candidate 1 (CASC1); dehydrogenase/reductase (SDR family) member 9 (DHRS9); disrupted in schizophrenia 1 (DISC1); eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2); uncharacterized LOC727820 (LOC727820); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6); RNA binding motif protein 47 (RBM47); RPTOR independent companion of MTOR complex 2 (RICTOR); sterile alpha motif domain containing 9-like (SAMD9L); scavenger receptor class F member 1 (SCARF1); solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36A1); signal transducer and activator of transcription 1, 91 kDa (STAT1); cytochrome c oxidase subunit Vb (COX5B); SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCA1); ubiquitin-like modifier activating enzyme 6 (UBA6); zinc finger CCCH-type antiviral 1 (ZC3HAV1); tyrosine kinase, non-receptor 2 (TNK2), and combinations thereof. See, Table 5 for a list of biomarkers identified as showing an increase in expression level.

In another embodiment, the expression level of the blood biomarker in the sample obtained from the subject is decreased as compared to the reference expression level of the biomarker. Suitable biomarkers that indicate a risk for suicide when the expression level decreases as compared to the reference expression level have been found to include, for example, cluster 4 antigen (CD24; CD24 molecule); ATPase type 13A2 (ATP13A2); epoxide hydrolase 1, microsomal (xenobiotic) (EPHX1); HtrA serine peptidase 1 (HTRA1); leptin receptor (LEPR); spectrin beta non-erythrocytic 1 (SPTBN1); muscleblind-like 2 (MBNL2); olfactory receptor family 2 subfamily J member 3 (OR2J3); Ras homolog enriched in brain (RHEB); glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 (GRINA); D-box binding protein, promyelocytic leukemia (PML), potassium inwardly-rectifying channel, subfamily J, member 2 (KCNJ2), topoisomerase (DNA) 1 (TOP1) and combinations thereof. See, Table 5 for a list of biomarkers identified as showing a decrease in expression level.

In another embodiment, the method includes determining the expression level of a blood biomarker in the sample obtained from the subject that is increased as compared to the reference expression level of the biomarker and determining the expression level of the blood biomarker in the sample obtained from the subject that is decreased as compared to the reference expression level of the biomarker. For example, spermidine/spermine N1-acetyltransferase 1 (SAT1); forkhead box N3 (FOXN3); guanylate binding protein 1 (GBP1); phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5); apolipoprotein L2 (APOL2); ATPase H+ transporting lysosomal 9 kDa, V0 subunit e1 (ATP6V0E1); GRINL1A complex locus (GCOM1); interleukin 1 beta (IL1B); lipoma HMGIC fusion partner (LHFP); lipase A (LIPA); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); phosphatase and tensin homolog (PTEN); reversion-inducing-cysteine-rich protein with kazal motifs (RECK); tumor necrosis factor (ligand) superfamily member 10 (TNFSF10); ATP-binding cassette, subfamily A (ABC1) member 1 (ABCA1); Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357); cancer susceptibility candidate 1 (CASC1); dehydrogenase/reductase (SDR family) member 9 (DHRS9); disrupted in schizophrenia 1 (DISC1); eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2); uncharacterized LOC727820 (LOC727820); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6); RNA binding motif protein 47 (RBM47); RPTOR independent companion of MTOR complex 2 (RICTOR); sterile alpha motif domain containing 9-like (SAMD9L); scavenger receptor class F member 1 (SCARF1); solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36A1); signal transducer and activator of transcription 1, 91 kDa (STAT1); cytochrome c oxidase subunit Vb (COX5B); SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCA1); ubiquitin-like modifier activating enzyme 6 (UBA6); zinc finger CCCH-type antiviral 1 (ZC3HAV1); tyrosine kinase, non-receptor 2 (TNK2), and combinations thereof in the blood sample of the subject can be increased as compared to the reference expression level, and cluster 4 antigen (CD24; CD24 molecule); ATPase type 13A2 (ATP13A2); epoxide hydrolase 1, microsomal (xenobiotic) (EPHX1); HtrA serine peptidase 1 (HTRA1); leptin receptor (LEPR); spectrin beta non-erythrocytic 1 (SPTBN1); muscleblind-like 2 (MBNL2); olfactory receptor family 2 subfamily J member 3 (OR2J3); Ras homolog enriched in brain (RHEB); glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 (GRINA); D-box binding protein, promyelocytic leukemia (PML), potassium inwardly-rectifying channel, subfamily J, member 2 (KCNJ2), topoisomerase (DNA) 1 (TOP1) and combinations thereof in the blood sample of the subject can be decreased as compared to the reference expression level to indicate an increase in the risk of suicide in a subject.

A particularly suitable sample for which the expression level of a biomarker is determined can be, for example, blood, including whole blood, leukocytes, and megakaryocytes. Other suitable samples for which the expression level of a biomarker is determined can be, for example, brain, cerebrospinal fluid, olfactory epithelium cells, fibroblasts from skin biopsies, induced pluripotent stem cells, and neuronal-like cells derived therefrom.

While described herein as a change in expression level, in some embodiments, particular levels of one or more of the above-described biomarkers can be useful for objectively identifying subjects at risk for future suicide. For example, it has been found that levels of SAT1 at 2500 Affymetrix microarray fluorescence intensity units (AU) or greater, including 2600 AU or greater, including 2700 AU or greater, including 2800 AU or greater, including 2900 AU or greater, and including 3000 AU or greater, have been found to be at increased risk for future suicide.

In another aspect, the present disclosure is directed to a method for monitoring response of a subject to a treatment for suicidal risk. As used herein, “treatment for suicidal risk” refers to a drug, nutritional, pharmaceutical, or the like, and combinations thereof that can modify the likelihood of a subject attempting and/or completing suicide. The method includes obtaining an expression level of a biomarker; administering a treatment for suicidal risk to the subject; and determining the expression level of the biomarker in a sample obtained from the subject after the treatment is administered, wherein a change in the expression level of the biomarker in the sample obtained from the subject after the treatment is administered as compared to the expression level of the biomarker before the treatment is administered indicates a response to the treatment.

Administration of the treatment can be by any suitable method known by those skilled in the art such as, for example, topical administration, enteral administration and parenteral administration. Suitable methods of administration can be, for example, transdermal administration, oral administration, and injection.

Suitable treatments for suicidal risk can be, for example, clozapine, omega-3 fatty acids (e.g., docosahexaenoic acid (DHA)), lithium, IL-1 trap, canakinumab, nicorandil, amiodarone, arsenic trioxide, vemurafenib, elsamitrucin, T 0128, CT-2106, BN80927, tafluposide, TAS-103, beta-lapachone, irinotecan, topo tecan, 9-amino-20-camptothecin, rubitecan, gimatecan, karenitecin, and combinations thereof.

Response to the treatment can be a decrease in the expression level of a biomarker after treatment. Biomarkers for which a decrease in the expression level of the biomarker indicates a response to the treatment can be, for example, spermidine/spermine N1-acetyltransferase 1 (SAT1); forkhead box N3 (FOXN3); guanylate binding protein 1 (GBP1); phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5); apolipoprotein L2 (APOL2); ATPase H+ transporting lysosomal 9 kDa, V0 subunit e1 (ATP6V0E1); GRINL1A complex locus (GCOM1); lipoma HMGIC fusion partner (LHFP); lipase A (LIPA); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); reversion-inducing-cysteine-rich protein with kazal motifs (RECK); tumor necrosis factor (ligand) superfamily member 10 (TNFSF10); ATP-binding cassette, subfamily A (ABC1) member 1 (ABCA1); Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357); cancer susceptibility candidate 1 (CASC1); dehydrogenase/reductase (SDR family) member 9 (DHRS9); disrupted in schizophrenia 1 (DISC1); eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2); uncharacterized LOC727820 (LOC727820); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6); RNA binding motif protein 47 (RBM47); RPTOR independent companion of MTOR complex 2 (RICTOR); sterile alpha motif domain containing 9-like (SAMD9L); scavenger receptor class F member 1 (SCARF1); solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36A1); signal transducer and activator of transcription 1, 91 kDa (STAT1); cytochrome c oxidase subunit Vb (COX5B); SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCA1); ubiquitin-like modifier activating enzyme 6 (UBA6); zinc finger CCCH-type antiviral 1 (ZC3HAV1); tyrosine kinase, non-receptor 2 (TNK2), and combinations thereof.

Response to the treatment can alternatively be an increase in the expression level of a biomarker after treatment. Biomarkers for which an increase in the expression level of the biomarker indicates a response to the treatment can be, for example, small cell lung carcinoma cluster 4 antigen (CD24; CD24 molecule); ATPase type 13A2 (ATP13A2); epoxide hydrolase 1, microsomal (xenobiotic) (EPHX1); HtrA serine peptidase 1 (HTRA1); leptin receptor (LEPR); spectrin beta non-erythrocytic 1 (SPTBN1); muscleblind-like 2 (MBNL2); olfactory receptor family 2 subfamily J member 3 (OR2J3); Ras homolog enriched in brain (RHEB); glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 (GRINA); D-box binding protein and combinations thereof.

Response to the treatment can be a decrease in the expression level of a first biomarker and an increase in a second biomarker. The first biomarker can be, for example, spermidine/spermine N1-acetyltransferase 1 (SAT1); forkhead box N3 (FOXN3); guanylate binding protein 1 (GBP1); phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5); apolipoprotein L2 (APOL2); ATPase H+ transporting lysosomal 9 kDa, V0 subunit e1 (ATP6V0E1); GRINL1A complex locus (GCOM1); lipoma HMGIC fusion partner (LHFP); lipase A (LIPA); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); reversion-inducing-cysteine-rich protein with kazal motifs (RECK); tumor necrosis factor (ligand) superfamily member 10 (TNFSF10); ATP-binding cassette, subfamily A (ABC1) member 1 (ABCA1); Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357); cancer susceptibility candidate 1 (CASC1); dehydrogenase/reductase (SDR family) member 9 (DHRS9); disrupted in schizophrenia 1 (DISC1); eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2); uncharacterized LOC727820 (LOC727820); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6); RNA binding motif protein 47 (RBM47); RPTOR independent companion of MTOR complex 2 (RICTOR); sterile alpha motif domain containing 9-like (SAMD9L); scavenger receptor class F member 1 (SCARF1); solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36A1); signal transducer and activator of transcription 1, 91 kDa (STAT1); cytochrome c oxidase subunit Vb (COX5B); SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCA1); ubiquitin-like modifier activating enzyme 6 (UBA6); zinc finger CCCH-type antiviral 1 (ZC3HAV1); and combinations thereof. The second biomarker can be, for example, cluster 4 antigen (CD24; CD24 molecule); ATPase type 13A2 (ATP13A2); epoxide hydrolase 1, microsomal (xenobiotic) (EPHX1); HtrA serine peptidase 1 (HTRA1); leptin receptor (LEPR); spectrin beta non-erythrocytic 1 (SPTBN1); muscleblind-like 2 (MBNL2); olfactory receptor family 2 subfamily J member 3 (OR2J3); Ras homolog enriched in brain (RHEB); glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 (GRINA); D-box binding protein, interleukin 1 beta (IL1B), phosphatase and tensin homolog (PTEN), promyelocytic leukemia (PML), potassium inwardly-rectifying channel, subfamily J, member 2 (KCNJ2), topoisomerase (DNA) 1 (TOP1) and combinations thereof. See, Table 5.

In another aspect, the present disclosure is directed to a method for determining suicidal risk as a side-effect of an antidepressant. The method includes obtaining an expression level of a biomarker from the subject; administering an antidepressant to the subject; and determining an expression level of the biomarker in a sample obtained from the subject after the antidepressant is administered. A change in the expression level of the biomarker in the sample obtained from the subject after the antidepressant is administered as compared to the expression level of the biomarker before the antidepressant is administered indicates suicidal risk as a side-effect of the antidepressant.

It is known that suicide risk is a rare, but very serious side-effect of some drugs. Upon initiation of antidepressant therapy, subjects can sometimes experience a sudden onset of suicidal ideation (e.g., suicidal thoughts and behaviors) that accompanies treatment. Subjects can become suicidal in the first weeks of treatment, upon a dosage change and/or a combination thereof. This has caused the U.S. Food and Drug Administration to require manufacturers to place explicit warnings on the label of the drug stating that its use may cause a risk of suicide.

Suitable antidepressants can be, for example, bupropion, citalopram, escitalopram, fluoxetine, fluvoxamine, mirtazapine, nefazodone, paroxetine, sertraline, and venlafaxine.

Suitable biomarkers can be, for example, spermidine/spermine N1-acetyltransferase 1 (SAT1); forkhead box N3 (FOXN3); guanylate binding protein 1 (GBP1); phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5); apolipoprotein L2 (APOL2); ATPase H+ transporting lysosomal 9 kDa, V0 subunit e1 (ATP6V0E1); GRINL1A complex locus (GCOM1); interleukin 1 beta (IL1B); lipoma HMGIC fusion partner (LHFP); lipase A (LIPA); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); phosphatase and tensin homolog (PTEN); reversion-inducing-cysteine-rich protein with kazal motifs (RECK); tumor necrosis factor (ligand) superfamily member 10 (TNFSF10); ATP-binding cassette, subfamily A (ABC1) member 1 (ABCA1); Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357); cancer susceptibility candidate 1 (CASC1); dehydrogenase/reductase (SDR family) member 9 (DHRS9); disrupted in schizophrenia 1 (DISC1); eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2); uncharacterized LOC727820 (LOC727820); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6); RNA binding motif protein 47 (RBM47); RPTOR independent companion of MTOR complex 2 (RICTOR); sterile alpha motif domain containing 9-like (SAMD9L); scavenger receptor class F member 1 (SCARF1); solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36A1); signal transducer and activator of transcription 1, 91 kDa (STAT1); cytochrome c oxidase subunit Vb (COX5B); SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCA1); ubiquitin-like modifier activating enzyme 6 (UBA6); zinc finger CCCH-type antiviral 1 (ZC3HAV1); tyrosine kinase, non-receptor 2 (TNK2); cluster 4 antigen (CD24; CD24 molecule); ATPase type 13A2 (ATP13A2); epoxide hydrolase 1, microsomal (xenobiotic) (EPHX1); HtrA serine peptidase 1 (HTRA1); leptin receptor (LEPR); spectrin beta non-erythrocytic 1 (SPTBN1); muscleblind-like 2 (MBNL2); olfactory receptor family 2 subfamily J member 3 (OR2J3); Ras homolog enriched in brain (RHEB); glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 (GRINA); D-box binding protein, promyelocytic leukemia (PML), potassium inwardly-rectifying channel, subfamily J, member 2 (KCNJ2), topoisomerase (DNA) 1 (TOP1) and combinations thereof. Particularly suitable biomarkers include SAT1, MARCKS, PTEN, MAP3K3, and combinations thereof.

In yet another aspect, the present disclosure is directed to a method of predicting hospitalization of a subject at risk of suicide. The method includes obtaining a first expression level of a blood biomarker in an initial sample obtained from the subject; and determining a second expression level of the blood biomarker in a subsequent sample obtained from the subject, wherein an increase in the expression level of the blood biomarker in the subsequent sample obtained from the subject as compared to the expression level of the initial sample indicates a higher risk of future hospitalizations due to suicidality.

Suitable biomarkers can be, for example, spermidine/spermine N1-acetyltransferase 1 (SAT1); myristoylated alanine-rich protein kinase C substrate (MARCKS); 6-phosphogluconolactonase (PGLS); phosphatase and tensin homolog (PTEN); mitogen-activated protein kinase kinase kinase 3 (MAP3K3); and combinations thereof.

EXAMPLES Example 1

Materials and Methods

In this Example, whole-genome gene expression profiling of blood samples was conducted to identify blood gene expression biomarkers for suicidality.

Human subjects. Male Caucasian subjects diagnosed with bipolar disorder (“Discovery Cohort”) were evaluated that had a diametrical change in suicidal ideation scores from no suicidal ideation to high suicidal ideation from visit to visit. The subjects were limited to minimize any potential gender-related and ethnicity-related state effects on gene expression. A demographic breakdown of the Discovery Cohort subjects is shown in Table 1A.

A “Validation Cohort”, in which the top biomarker findings from the Discovery Cohort testing were evaluated, consisted of an age-matched cohort of 9 male suicide completers obtained through the Marion County Coroner's Office (8 Caucasians, 1 African-American) (Table 1B). The subjects in the Validation Cohort were required to have a last observed alive post-mortem interval of 24 hours or less, and had to have completed suicide by means other than overdose, which could affect gene expression.

TABLE 1 Demographics (1) Detailed. (2) Aggregate. Diagnosis established by comprehensive structured clinical interview (DIGS). NOS—not otherwise specified. Suicidal Ideation question is from the Hamilton Rating Scale for Depression obtained at the time of the blood draw for each subject. (1) A. Discovery Cohort Suicidal SubjectID-Visit Diagnosis Age Gender Ethnicity Ideation phchp023v1 Bipolar Disorder 52 M Caucasian 0 NOS phchp023v2 Bipolar Disorder 52 M Caucasian 3 NOS phchp023v3 Bipolar Disorder 52 M Caucasian 0 NOS phchp093v1 Bipolar I 51 M Caucasian 0 Disorder phchp093v2 Bipolar I 51 M Caucasian 0 Disorder phchp093v3 Bipolar I 52 M Caucasian 3 Disorder phchp095v1 Bipolar I 28 M Caucasian 3 Disorder phchp095v2 Bipolar I 29 M Caucasian 0 Disorder phchp095v3 Bipolar I 29 M Caucasian 2 Disorder phchp122v1 Bipolar Disorder 51 M Caucasian 0 NOS phchp122v2 Bipolar Disorder 51 M Caucasian 2 NOS phchp128v1 Bipolar I 45 M Caucasian 2 Disorder phchp128v2 Bipolar I 45 M Caucasian 0 Disorder phchp136v1 Bipolar I 41 M Caucasian 0 Disorder phchp136v2 Bipolar I 41 M Caucasian 0 Disorder phchp136v3 Bipolar I 41 M Caucasian 3 Disorder phchp153v1 Bipolar II 55 M Caucasian 0 Disorder phchp153v2 Bipolar II 55 M Caucasian 2 Disorder phchp153v3 Bipolar II 56 M Caucasian 0 Disorder phchp179v1 Bipolar Disorder 36 M Caucasian 0 NOS phchp179v2 Bipolar Disorder 37 M Caucasian 0 NOS phchp179v3 Bipolar Disorder 37 M Caucasian 3 NOS phchp183v1 Bipolar I 48 M Caucasian 3 Disorder phchp183v2 Bipolar I 48 M Caucasian 0 Disorder B. Validation Cohort SubjectID Psychiatric Diagnosis Age Gender Ethnicity Suicide INBR009 Bipolar/ 59 M Caucasian Hanging Schizophrenia INBR011 Depression/ADHD 26 M Caucasian GSW to chest INBR012 Unknown 39 M Caucasian GSW to head INBR013 Depression 68 M African-American GSW to mouth INBR014 None 27 M Caucasian Hanging INBR015 None 40 M Caucasian Hanging INBR016 Anxiety/TBI 68 M Caucasian GSW to head INBR017 Depression 56 M Caucasian GSW to chest INBR018 None 65 M Caucasian Slit wrist (2) Discovery Cohort Suicidal Ideation SI (score) No SI (0) High SI (2-4) Overall Number of 9(14) 9(10) 9(24) subjects (number of chips) Age mean 46.1 43.8 45.1 years (SD) (8.1) (9.7) (8.7) range 29-56 28-55 28-56 Ethnicity # (9/0) (9/0) (9/0) subjects (Caucasian/ African- American) Test Suicide Cohort Completers Number of 9(9) subjects (number of chips) Age mean years 49.8 (SD) (17) range 26-68 Ethnicity # (8/1) subjects (Caucasian/ African- American)

The Discovery Cohort subjects were on a variety of different psychiatric medications, including mood stabilizers, antidepressants, antipsychotics, benzodiazepines, and others as listed in Table 2A (Table 2B provides toxicology for subjects in the coroner's office test cohort-suicide completers). Medications can have a strong influence on gene expression. However, this Example tested differentially expressed genes based at on intra-subject analyses, which factor out not only genetic background, effects but also medication effects. Moreover, there was no consistent pattern found in any particular type of medication, or between any change in medications and suicidal ideation in the rare instances where there were changes in medications between visits. Subjects were excluded, however, if they had significant acute medical or neurological illnesses, or had evidence of active substance abuse or dependence.

TABLE 2A Psychiatric medications of Discovery Cohort subjects. SubjectID-Visit Psychiatric Medications phchp023v1 FLEXARIL 10 MG FOR SLEEP PRN LAMOTRIGINE 200 MG ZIPRASIDONE 60 MG v2 FLEXARIL 10 MG FOR SLEEP PRN LAMOTRIGINE 200 MG ZIPRASIDONE 60 MG v3 FLEXARIL 10 MG FOR SLEEP PRN LAMOTRIGINE 200 MG ZIPRASIDONE 60 MG Phchp093v1 CITALOPRAM HYDROBROMIDE 40 MG TAB TAKE ONE-HALF TABLET ORALLY EVERY DAY VALPROIC ACID 500 MG 24 HR (ER) SA TAB TAKE THREE TABLETS ORALLY AT BEDTIME QUETIAPINE FUMARATE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME GABAPENTIN 300 MG CAP TAKE ONE CAPSULE ORALLY AT BEDTIME FOR 3 DAYS, THEN TAKE ONE CAPSULE, TWICE A DAY QUETIAPINE FUMARATE 25 MG TAB TAKE ONE TABLET ORALLY EVERY DAY AS NEEDED v2 CITALOPRAM HYDROBROMIDE 40 MG TAB TAKE ONE-HALF TABLET ORALLY EVERY DAY VALPROIC ACID 500 MG 24 HR (ER) SA TAB TAKE THREE TABLETS ORALLY AT BEDTIME DOXEPIN HCL 10 MG CAP TAKE ONE CAPSULE ORALLY AT BEDTIME GABAPENTIN 300 MG CAP TAKE TWO CAPSULES ORALLY TWICE A DAY AND TAKE THREE CAPSULES AT BEDTIME QUETIAPINE FUMARATE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME QUETIAPINE FUMARATE 25 MG TAB TAKE ONE TABLET ORALLY EVERY DAY v3 CITALOPRAM HYDROBROMIDE 40 MG TAB TAKE ONE-HALF TABLET ORALLY EVERY DAY VALPROIC ACID 500 MG 24 HR (ER) SA TAB TAKE THREE TABLETS ORALLY AT BEDTIME DOXEPIN HCL 10 MG CAP TAKE ONE CAPSULE OLLY AT BEDTIME GABAPENTIN 300 MG CAP TAKE TWO CAPSULES ORALLY TWICE A DAY WITH FOOD QUETIAPINE FUMARATE 100 MG TAB TAKE ONE TABLET ORALLY PENDING AT BEDTIME QUETIAPINE FUMARATE 25 MG TAB TAKE ONE TABLET ORALLY EVERY DAY Phchp095v1 VALPROIC ACID 250 MG 24 HR (ER) SA TAB TAKE SEVEN TABLETS ORALLY AT BEDTIME RISPERIDONE 2 MG TAB TAKE ONE TABLET ORALLY EVERY DAY SERTRALILNE HCL 100 MG TAB TAKE ONE TABLET ORALLY EVERY DAY v2 VALPROIC ACID 250 MG 24 HR (ER) SA TAB TAKE SEVEN TABLETS ORALLY AT BEDTIME RISPERIDONE 2 MG TAB TAKE ONE TABLET ORALLY EVERY DAY SERTRALINE HCL 100 MG TAB TAKE ONE TABLET ORALLY EVERY DAY v3 BENZTROPINE MESYLATE ORAL 1 MG TAB TAKE ONE TABLET ORALLY TWICE A DAY TRAZODONE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME RISPERIDONE 4 MG TAB TAKE ONE TABLET ORALLY EVERY DAY LORAZEPAM INJ IM Q4H PRN 2 MG/1 ML LORAZEPAM TAB PO Q6H PRN 2 MG Phchp122v1 AMITRIPTYLINE HCL 10 MG TAB TAKE ONE TABLET ORALLY THREE TIMES DAILY AT 10AM, 2PM AND 10PM LEVETIRACETAM 500 MG TAB TAKE ONE TABLET ORALLY TWICE A DAY LORAZEPAM 0.5 MG TAB TAKE 1 TABLET ORALLY TWICE A DAY LUBRICATING (PF) OPH OINT APPLY ½ INCH RIBBON IN BOTH EYES AT BEDTIME RISPERIDONE 4 MG TAB TAKE ONE-HALF TABLET ORALLY AT BEDTIME TOPIRAMATE 25 MG TAB TAKE ONE TABLET ORALLY TWICE A DAY; INCREASE AS DIRECTED TO TWO TABLETS TWICE A DAY TRAZODONE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME AS NEEDED FOR INSOMNIA v2 VALPROIC ACID 500 MG 24 HR (ER) SA TAB TAKE TWO TABLETS ORALLY AT BEDTIME LORAZEPAM 1 MG TAB TAKE TWO TABLETS ORALLY AT BEDTIME AS NEEDED FOR INSOMNIA MIRTAZAPINE 30 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME PRAZOSIN 2 MG CAP TAKE ONE CAPSULE ORALLY TWICE A DAY. TAKE SECOND DOSE AT BEDTIME. VENLAFAXINE HCL 150 MG 24 HR SA TAB TAKE ONE TABLET ORALLY TWICE A DAY (BREAKFAST AND LUNCH) ZIPRASIDON 80 MG CAP TAKE TWO CAPSULES ORALLY EVERY EVENING WITH DINNER phchpl28vl DISULFIRAM 250 MG TAB TAKE ONE TABLET ORALLY EVERY DAY VALPROIC ACID 500 MG 24 HR (ER) SA TAB TAKE THREE TABLETS ORALLY AT BEDTIME TRAZODONE 50 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME AS NEEDED FOR INSOMNIA V2 DISULFIRAM 250 MG TAB TAKE ONE TABLET ORALLY EVERY DAY VALPROIC ACID 500 MG 24 HR (ER) SA TAB TAKE THREE TABLETS ORALLY AT BEDTIME TRAZODONE 50 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME AS NEEDED FOR INSOMNIA PHCHP136V1 BENZTROPINE MESYLATE ORAL MESYLATE 1 MG TAB TAKE ONE TABLET ORALLY TWICE A DAY CHLORPROMAZINE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME HALOPERIDOL DECANOATE 5 ML(100 MG/ML) INJ INJECT 200 MG HOLD(2 ML) INTRAMUSCULAR EVERY 4 WEEKS OXCARBAZEPINE 300 MG TAB TAKE ONE TABLET ORALLY EVERY MORNING AND TAKE THREE TABLETS AT BEDTIME FISH OIL CAP/TAB v2 BENZTROPINE MESYLATE ORAL MESYLATE 2 MG TAB TAKE ONE TABLET ORALLY TWICE A DAY CHLORPROMAZINE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME HALOPERIDOL DECANOATE 5 ML(100 MG/ML) NJ INJECT 200 MG HOLD (2 ML) INTRAMUSCULAR EVERY 4 WEEKS OXCARBAZEPINE 300 MG TAB TAKE ONE TABLET ORALLY EVERY MORNING AND TAKE THREE TABLETS AT BEDTIME v3 BENZTROPINE MESYLATE ORAL MESYLATE 2 MG TAB TAKE ONE TABLET ORALLY TWICE A DAY CHLORPROMAZINE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME HALOPERIDOL DECANOATE 5 ML(100 MG/ML) INJ INJECT 200 MG HOLD(2 ML) INTRAMUSCULAR EVERY 4 WEEKS OXCARBAZEPINE 300 MG TAB TAKE ONE TABLET ORALLY EVERY MORNING AND TAKE THREE TABLETS AT BEDTIME Phchp153v1 TRAZODONE 50 MG TAB TAKE ONE TO ONE AND ONE-HALF TABLETS ORALLY AT BEDTIME VENLAFAXINE HCL 225 MG 24 HR SA TAB TAKE ONE TABLET ORALLY EVERY DAY WITH BREAKFAST v2 TRAZODONE 100 MG TAB TAKE ONE TABLET ORALLY AT BEDTIME VENLAFAXINE HCL 225 MG 24 HR SA TAB TAKE ONE TABLET ORALLY EVERY DAY WITH BREAKFAST v3 VENLAFAXINE HCL 150 MG 24 HR SA TAB-1X PER DAY TRAZADONE HCL 50 MG-1X PER DAY Phchp179v1 LISDEXAMFETAMINE (40 MG) QUETIAPINE (600 MG) PAROXETINE (30 MG) ALPRAZOLAM (½ MG PER NIGHT) ZOLPIDEM (10 MG PER NIGHT) v2 No Psychiatric Medication v3 QUETIAPINE 100 MG-IS BEING TAPERED OFF ZIPRASIDONE 120 MG PAROXETINE 30 MG ALPRAZOLAM unknown dosage, PRN LISDEXAMFETAMINE 50 MG PHCHP183V1 ARIPIPRAZOLE TAB 20 MG PO DAILY BENZTROPINE MESYLATE ORAL TAB 1 MG PO Q4H PRN VALPROIC ACID TAB, SA, 24 HR (EXPENDED 2000 MG PO BEDTIME HALOPERIDOL INJ, SOLN 5 MG IM Q4H PRN HALOPERIDOL TAB 5 MG PO Q4H PRN HYDROXYZINE PAMOATE CAP, ORAL 25 MG PO Q6H PRN LORAZEPAM INJ 2 MG/1 ML IM Q4H PRN RISPERIDONE TAB 1 MG PO BID HYDROXYZINE PAMOATE 25 MG CAP TAKE ONE CAPSULE ORALLY EVERY 6 HOURS AS NEEDED FOR ANXIETY RISPERIDONE 1 MG TAB TAKE ONE-HALF TABLET ORALLY TWICE A DAY FISH OIL CAP/TAB ORALLY v2 ARIPIPRAZOLE 20 MG TAB TAKE ONE TABLET ORALLY EVERY DAY HYDROXYZINE PAMOATE 25 MG CAP TAKE ONE CAPSULE ORALLY EVERY 6 HOURS AS NEEDED FOR ANXIETY CITALOPRAM HYDROBROMIDE 10 MG TAB TAKE ONE-HALF TABLET ORALLY EVERY MORNING

TABLE 2B Toxicology for subject in the coroner's office test cohort- suicide completers SubjectID Toxicology INBR009 INBR011 ALPRAZOLAM 3.2 NG/ML TRAMADOL 331 NG/ML NORTRAMADOL 179 NG/ML BUPROPION 136 NG/ML CITALOPRAM/ESCITALOPRAM 229 NG/ML CAFFEINE POSITIVE COTININE POSITIVE INBR012 Not Available INBR013 CAFFEINE POSITIVE INBR014 ETHANOL 0.15% (W/V) CAFFEINE INBR015 ETHANOL 0.119% (W/V) CAFFEINE INBR016 Not Available INBR017 Not Available INBR018 ETHANOL 0.057% (W/V) AMIODARONE CAFFEINE COTININE

The subjects were subjected to diagnostic assessments using Diagnostic Interview for Genetic Studies, which is the scale used by the Genetics Initiative Consortia for both bipolar disorder and major depression, at a baseline visit, followed by up to three testing visits, three to six months apart. Particularly, six subjects were subjected to three follow-up testing visits and three subjects were subjected to two follow-up testing visits, resulting in a total of 24 blood samples for subsequent microarray studies as discussed herein. At each testing visit, the subjects received a series of psychiatric rating scales, including the Hamilton Rating Scale for Depression (HAMD-17), which includes a suicidal ideation rating item (FIG. 1B), and blood was drawn. The suicidal ideation scores varied during the visits from no ideation to high suicidal ideation.

Gene Expression Analysis

Using the nine subjects with multiple visits, corresponding to 24 chips, from the Discovery Cohort a differential analysis was run using Partek Genomic Suites 6.6 software package (Partek Incorporated, St. Louis, Mo.). Normalization was performed on all 24 chips by robust multi-array analysis (RMA), background corrected with quartile normalization and a median polish probe set summarization of all 24 chips to obtain the normalized expression levels of all probe sets for each chip. Two analyses, an intra-subject analysis and an inter-subject analysis, were conducted to establish a list of differentially expressed probe sets.

RNA extraction. During each visit, from about 2.5 ml to about 5.0 ml of whole blood was collected from the subjects separately into two PaxGene tubes, treated to stabilize RNA, by routine venipuncture. The cells from the whole blood were concentrated by centrifugation, the pellet washed, resuspended and incubated in buffers containing proteinase K for protein digestion. A second centrifugation step was conducted to remove residual cell debris. Ethanol was added. After ethanol addition, the supernatant was applied to a silica-gel membrane/column. The column was centrifuged and contaminants were removed in three wash steps. RNA bound to the membrane was then eluted using DEPC-treated water.

Globin reduction. To remove globin mRNA, total RNA from the whole blood was mixed with a biotinylated Capture Oligo Mix that is specific for human globin mRNA. The mixture was then incubated for 15 minutes to allow the biotinylated oligonucleotides to hybridize with the globin mRNA. Streptavidin magnetic beads were then added, and the mixture was incubated for 30 minutes. During this incubation, streptavidin binds to the biotinylated oligonucleotides, thereby capturing the globin mRNA on the magnetic beads. The streptavidin magnetic beads were then pulled to the side of the tube with a magnet, and the RNA, depleted of the globin mRNA, was transferred to a fresh tube. The treated RNA was further purified using a rapid magnetic bead-based purification method consisting of adding an RNA binding bead suspension to the samples and using magnetic capture to wash and elute the globin-clear RNA.

Sample labeling. Samples were labeled using an Ambion MessageAmp II-BiotinEnhanced antisense RNA (aRNA) amplification kit. The procedure involved the following steps:

-   -   1) Reverse transcription to synthesize first strand cDNA was         primed with T7 Oligo(dT) primer to synthesize cDNA containing a         T7 promoter sequence.     -   2) Second strand cDNA synthesis converted the single-stranded         cDNA into a double-stranded DNA (dsDNA) template for         transcription. The reaction employed DNA polymerase and RNase H         to simultaneously degrade the RNA and synthesize second strand         cDNA.     -   3) cDNA purification removed RNA, primers, enzymes, and salts         that would inhibit in vitro transcription.     -   4) In vitro transcription to synthesize aRNA with biotin-NTP mix         generated multiple copies of biotin-modified aRNA from the         double-stranded cDNA templates; this was the amplification step.     -   5) aRNA purification removed unincorporated NTPs, salts,         enzymes, and inorganic phosphate to improve the stability of the         biotin-modified aRNA.     -   6) aRNA fragmentation in a reaction that employs a metal-induced         hydrolysis. The fragmented labeled aRNA is then ready for         hybridization to the Affymetrix microarray chip.

Microarrays. Biotin-labeled aRNA was then hybridized to Affymetrix HG-U133 Plus 2.0 GeneChips (Affymetrix, Santa Clara, Calif.) with over 40,000 genes and expressed sequence tags (ESTs) according to manufacturer's protocols (www.affymetrix.com/support/technical/manual/expression_manual.affx). All GAPDH 3′/5′ ratios should be less than 2.0 and backgrounds under 50. Arrays were stained using standard Affymetrix protocols for antibody signal amplification and scanned on an Affymetrix GeneArray 2500 scanner with a target intensity set at 250. Present/absent calls were determined using GCOS software with thresholds set at default values. Quality control measures including 30/50 ratios for glyceraldehyde 3-phosphate dehydrogenase and b-actin, scale factors, background and Q values were within acceptable limits.

Analysis. Each subject's suicidal ideation (SI) scores at time of blood collection (0—no SI compared to 2 and above—high SI) were used for analysis. Particularly, gene expression differences between the no SI and the high SI states using both an intra-subject and an inter-subject design as shown in FIG. 1A were analyzed.

An intra-subject analysis using a fold change in expression of at least 1.2 between high and no suicidal ideation visits within each subject was performed. There were in total 15 comparisons. Probe sets that had a 1.2 fold change were then assigned either a 1 (increased in high suicidal ideation) or a −1 (decreased in high suicidal ideation) in each comparison. These values were then summed for each probe set across the 15 comparisons, yielding a range of scores between −11 and 12. Probe sets in the top 5% (1,269 probe sets, <5% of 54,675 total probe sets) had an absolute value of 7 and greater, receiving an internal Convergent Functional Genomics (CFG) score of 1 point. Those probe sets in the top 0.1% (24 probe sets, <0.1% of 54,675 total probe sets) had a total absolute value of 11 and greater and received an internal CFG score of 3 points.

Additionally, an inter-subject analysis using t-test (2-tailed, unequal variance) was performed to find probes differentially expressed between high suicidal ideation and no suicidal ideation chips (FIG. 1A), resulting in 648 probe sets with P<0.05. Probe sets with a P<0.05 received an internal CFG score of 1 point, while probe sets with P<0.001 received 3 points.

Results were then further filtered by only selecting probe sets that overlapped between the intra-subject and the inter-subject analyses, resulting in 279 probe sets corresponding to 246 unique genes. Gene names for the probe sets were identified using Partek as well as NetAffyx (Affymetrix) for Affymetrix HG-U133 Plus 2.0 GeneChips, followed by GeneCards to confirm the primary gene symbol. In addition, for those probe sets that were not assigned a gene name by Partek or NetAffyx, the UCSC Genome Browser on Human February 2009 (GRCh37/hg19) was used to directly map them to known genes. Genes were then scored using manually curated CFG databases as described below and shown in FIG. 2.

Convergent Functional Genomics (CFG) Databases

Manually curated databases were created in the Laboratory of Neurophenomics, Indiana University School of Medicine (www.neurophenomics.info) of all the human gene expression (postmortem brain, blood, cell cultures), human genetic (association, CNVs, linkage) and animal model gene expression and genetic studies published to date on psychiatric disorders. Only the findings deemed significant in the primary publication, by the study authors, using their particular experimental design and thresholds, were included in the databases. The databases included only primary literature data and did not include review papers or other secondary data integration analyses to avoid redundancy and circularity. These large and constantly updated databases have been used in previous CFG cross-validation and prioritization studies.

Human Postmortem Brain Gene Expression Evidence. Information about genes was obtained and imported in the databases searching the primary literature with PubMed (ncbi.nlm.nih.gov/PubMed), using various combinations of keywords (e.g., gene name and suicide and human brain). Postmortem convergence was deemed to occur for a gene if there were published reports of human postmortem data showing changes in expression of that gene in brains from patients who died from suicide.

Human Genetic Evidence Association and Linkage. To designate convergence for a particular gene, the gene had to have independent published evidence of association or linkage for suicide. For linkage, the location of each gene was obtained through GeneCards (www.genecards.org), and the sex averaged cM location of the start of the gene was then obtained through compgen.rutgers.edu/old/map-interpolator/. For linkage convergence, the start of the gene had to map within 5cM of the location of a marker linked to the disorder.

CFG Scoring. For CFG analysis, two external cross-validating lines of evidence were weighed such that findings in human postmortem brain tissue, the target organ, were prioritized over genetic findings. Human brain expression evidence was given 4 points, while human genetic evidence was given a maximum of 2 points for association, and 1 point for linkage. Each line of evidence was capped in such a way that any positive findings within that line of evidence resulted in maximum points regardless of how many different studies support that single line of evidence, to avoid potential popularity biases.

In addition to the above external CFG score, genes based upon the initial differential expression analyses used to identify them were also prioritized. Probe sets identified by differential expression analyses could receive a maximum of 6 points (1 or 3 points from intra-subject analyses, and 1 or 3 points from inter-subject analyses). Thus, the maximum possible total CFG score for each gene was 12 points (6 points for internal score+6 points for external score).

The above-described scoring system provided a good separation of genes based on differential expression and on independent cross-validating evidence in the field (FIG. 2).

Pathway Analyses

IPA 9.0 (Ingenuity Systems, www.ingenuity.com, Redwood City, Calif.) was used to analyze the biological roles, including top canonical pathways and diseases, of the candidate genes resulting from the above findings (Table 3), as well as used to identify genes in the data sets that were the target of existing drugs (Table 4). Pathways were identified from the IPA library of canonical pathways that were most significantly associated with genes in the data set. The significance of the association between the data set and the canonical pathway was measured in two ways: 1) a ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules that map to the canonical pathway is displayed; and 2) Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the data set and the canonical pathway was explained by chance alone. A KEGG pathway analysis through the Partek Genomic Suites 6.6 software package was also conducted.

TABLE 3 Ingenuity Pathway Analyses. A. Pathways. B. Disease and Disorders. A. INGENUITY Pathways KEGG Pathways Top Canonical Pathway Enrichment Enrichment # Pathways P-Value Ratio Name Score p-value CFG 1 Role of Tissue 2.63E−04 3/115 Apoptosis 6.69102 0.001242 score >= 6.0 Factor in Cancer (0.026) N = 21 genes 2 Dendritic Cell 9.83E−04 3/207 Measles 6.06369 0.002326 Maturation (0.014) 3 Melanoma 1.13E−03 2/46 Endometrial 4.96787 0.006958 Signaling (0.043) cancer 4 Docosahexaenoic 1.18E−03 2/49 Influenza 4.90223 0.00743 Acid (DHA) (0.041) A Signaling 5 Endometrial 1.69E−03 2/57 Phosphatidyl- 4.85448 0.007793 Cancer Signaling (0.035) inositol signaling system CFG 1 NF-kB Signaling 4.42E−04 4/175 Measles 8.7667 0.000156 score >= 4.0 (0.023) N = 41 genes 2 Dendritic Cell 5.38E−04 4/207 Influenza 6.87308 0.001035 Maturation (0.019) A 3 PDGF Signaling  7.5E−04 3/85 mTOR 6.34986 0.001747 (0.035) signaling pathway 4 Role of Pattern 1.14E−03 3/106 Apoptosis 4.75687 0.008592 Recognition (0.028) Receptors in Recognition of Bacteria and Viruses 5 Role of Tissue 1.78E−03 3/115 Toll-like 4.37269 0.012617 Factor in Cancer (0.026) receptor signaling pathway All genes 1 Retinoic acid 1.12E−03 5/69 Ubiquitin 4.80416 0.0081956 differentially Mediated (0.072) mediated expressed Apoptosis proteolysis N = 246 genes Signaling (279 probe 2 Role of PKR in 1.19E−03 4/46 Herpes 4.14288 0.0158771 sets) Interferon (0.087) simplex Induction and infection Antiviral Response 3 UVA-induced 3.90E−03 5/92 Phagosome 4.0301 0.0177725 MAPK Signaling (0.054) 4 Dendritic Cell 4.71E−03 7/207 Measles 3.72158 0.0241958 Maturation (0.034) 5 Role of Pattern 5.38E−03 5/106 Influenza 5.03358 0.0065155 Recognition (0.047) A Receptors in Recognition of Bacteria and Viruses B. INGENUITY # Diseases and Disorders P-Value # Molecules CFG 1 Cancer 1.22E−06-4.54E−03 14 score >= 6.0 2 Connective Tissue 2.19E−04-3.41E−03 8 N = 21 genes Disorders 3 Inflammatory Disease 2.19E−04-4.54E−03 8 4 Skeletal and Muscular 2.19E−04-4.42E−03 9 Disorders 5 Gastrointestinal Disease 2.22E−04-4.54E−03 12 CFG 1 Cancer 4.51E−06-6.45E−03 20 score >= 4.0 2 Inflammatory Response 2.70E−05-6.45E−03 12 N = 41 genes 3 Antimicrobial Response 9.95E−05-6.45E−03 4 4 Infectious Disease 1.25E−04-5.52E−03 6 5 Connective Tissue 1.53E−04-6.45E−03 11 Disorders

TABLE 4 Ingenuity drug targets analysis. Repositioning of existing drugs for treating suicidality. CFG Direction Score of change Location Type(s) Drug(s) IL1B 8 I Extracellular cytokine IL-1 trap, interleukin 1, beta Space canakinumab KCNJ2 4 I Plasma ion channel nicorandil, potassium inwardly-rectifying Membrane amiodarone channel, subfamily J, member 2 PML 4 I Nucleus transcription arsenic trioxide promyelocytic leukemia regulator TNK2 4 D Cytoplasm kinase vemurafenib tyrosine kinase, non-receptor, 2 TOP1 2 I Nucleus enzyme elsamitrucin, T topoisomerase (DNA) I 0128,CT-2106, BN 80927, tafluposide, TAS- 103, beta- lapachone, irinotecan, topotecan, 9- amino-20- camptothecin, rubitecan, gimatecan, karenitecin Validation Analyses

The nine Affymetrix microarray data files from the Validation Cohort was imported as .cel files into Partek Genomic Suites 6.6 software package (Partek Incorporated St. Louis, Mo.). A robust multi-array analysis (RMA), background corrected with quartile normalization and a median polish probe set summarization of all 24+9=33 chips was conducted to obtain the normalized expression levels of all probe sets for each chip. Partek normalizes expression data into a log base of 2 for visualization. The data was non-log by taking 2 to the power of the transformed expression value. The non-log transformed expression data was then used to compare expression levels of SAT1 and CD24 in the different groups (FIG. 3G).

Further, testing of the top candidate biomarkers for suicidality were conduct (see FIGS. 3H and 3I). As shown, thirteen of the 41 CFG-top scoring biomarkers from Table 5 below showed step-wise significant change from no suicide ideation to high suicide ideation, to the validation suicide completers group. Six of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. The top CFG scoring biomarker, SAT1, remained the top biomarker after validation.

Results

Whole-genome gene expression profiling in blood samples from a longitudinally-followed homogeneous cohort of male subjects with a major mood disorder (bipolar disorder) that predisposes to suicidality was conducted. The samples were collected at repeated visits, 3 to 6 months apart. State information about suicidal ideation was collected from a questionnaire administered at the time of each blood draw. An intra-subject design was used to analyze data from 9 subjects that switched from no suicidal ideation to high suicidal ideation at different visits, which factors out genetic variability, as well as some medications, lifestyle and demographic variability. An inter-subject case-case analysis was also used to identify genes differentially expressed in the blood in no suicidal ideation states versus high suicidal ideation states. The top 0.1% and 5% of the gene expression probe sets distributions were considered and differentially scored. Overlap between the intra-subject and inter-subject analyses of gene expression changes was required. Such a restrictive approach was used as a way of minimizing false positives, even at the risk of having false negatives. For example, there were genes on each of the two lists, from intra- and inter-subject analyses, that had clear prior evidence for involvement in suicidality, such as MT1E (Sequeira A. et al., Gene expression changes in the prefrontral cortex, anterior cingulate cortex and nucleus accumbens of mood disorders subjects that committed suicide, PioS one 7, e35367, doi:10,1371/journal.pone.0035367 (2012)), respectively GSK3B (Karege F. et al., Alteration in kinase activity but not in protein levels of protein kinase B and glycogen synthase kinase-3beta in ventral prefrontal cortex of depressed suicide victims. Biol Psychiatry 61, 240-245, doi:10.1016/j.biopsych.2006.04.036 (2007)), but were not included in the subsequent analyses because they were not in the overlap.

A CFG approach was then used to cross-match the list of 246 overlapping top differentially expressed genes from the blood samples with other key lines of evidence (human postmortem brain data, human genetic data) implicating them in suicidality, as a way of identifying and prioritizing disease-relevant genomic biomarkers, extracting generalizable signal out of potential cohort-specific residual noise and genetic heterogeneity. Manually curated databases of the psychiatric genomic and proteomic literature to date was created and used in the CFG analyses. The CFG approach was thus a de facto field-wide collaboration. In essence, in a Bayesian fashion, the whole body of knowledge in the field was used to leverage findings from the Discovery Cohort data sets. Unlike the use of CFG in previous studies, no human peripheral tissue evidence from the literature was used as there was none directly matching the instant genes, reflecting perhaps the dearth of peripheral gene expression work done so far on suicides, and the need for a study like the instant Example. Animal model evidence was also not used as there were to date no clear studies in animal models of self-harm or suicidality. SAT1 (spermidine/spermine N1-acetyltransferase 1) was the top blood biomarker increased in suicidal states (i.e. the top risk marker), and CD24 (CD24 molecule; small cell lung carcinoma cluster 4 antigen) was the top blood biomarker decreased in suicidal states (i.e. the top protective marker) (FIG. 2 and Table 5).

TABLE 5 Top gene expression biomarkers for suicidality Change Differential Prior Human Prior Human Total Gene Symbol/ (I = Increase) Expression Genetic Brain Expression CFG Gene name Probe-sets (D = Decrease) Score Evidence Evidence Score SAT1 203455_s_at I 2 (Assoc) Suicide in 8 spermidine/spermine N1- Suicide Depression (D) acetyltransferase 1 attempt PFC (Chen. Fiori (Fiori. et al., 2010) Wanner Suicide(D) et al. AMY, PFC, HIP, 2010). THAL Suicide (Fiori, Bureau et (Sequeira, al. 2011) Gwadry Suicide(D) PFC et al. (Flori and 2006) Turacki 2011) Suicide (D) PFC (Fiori, Mechawar et al. 2009) Suicide (D) PFC (Fiori, Zouk et al. 2011) Suicide(D) PFC (Guipponi, Deutsch et al. 2009) Suicide(D)PFC (Klempan, T. A. et al 2009) Suicide(D)PFC (Sequeira, A. et al. 2006) CD24 209772_s_at D 4 Suicide in mood 8 CD24 molecule disorder)(D)NAC (Sequeira A. et al. 2012) FOXN3 230790_x_at I 2 (Assoc) Suicide 8 forkhead box N3 Suicide (I) PFC (Galfalvy, (Galfalvy, H. et H. et al. al. 2011) 2011) GBP1 231577_x_at I 4 Suicide in mood 8 guanylate binding protein 202269_x_at 2 disorders (D) 6 1, interferon-inducible, 202270_at 2 NAC (Karege, F. 6 67 kDa et al. 2007). PIK3R5 227553_at I 4 Suicide in mood 8 Phosphoinositide-3- disorder (D) PFC kinase, regulatory (Seqeira, Morgan subunit5 et al. 2012) APOL2 221653_x_at I 2 Suicide 6 Apolipoprotein L2 PFC (I) (Kekesi, K. A. et al. 2012) ATP13A2 218608_at D 2 Suicide(D) 6 ATPase type 13A2 (Sequeira, A. et al. 2012) ATP6V0E1 214149_s_at I 2 Suicide(D)PFC 6 ATPase, H+ transporting, 214244_s_at (Sequeira A. et lysosomal 9 kDa, al. 2006) V0 subunit e1 EPHX1 202017_at D 2 Suicide in 6 epoxide hydrolase 1, Schizophrenia microsomal (xenobiotic) (D) PFC (Kim, Choi et al. 2007) GCOM1 239099_at I 2 Suicide in 6 GRINL1A complex locus Depression (D) Klempan T A, 2009 HTRA1 201185_at D 2 Suicide(I) 6 HtrA serine peptidase 1 (Sequeria, A. et al. 2012) IL1B 39402_at I 2 Suicide(1) PFC 6 interleukin 1, beta (Pandey, G. N. et al., 2012) LEPR 211354_s_at D 2 Suicide(D) PFC 6 leptin receptor (Klempan, T. A. et al. 2009) Suicide(D) PFC (Lalovic, Klempen et al. 2010) Suicide(D) HIP (Sequeria, A. et al. 2007) Suicide in Depression (I) PFC (Zhurov V. et al. 2012) LHFP 218656_s_at I 2 Suicide in mood 6 lipoma HMGIC fusion disorder (I) NAC partner (Sequeria, A. et al. 2012) LIPA 236156_at I 2 Violent Suicide 6 lipase A (I) PFC (Freemantle, E et al. 2013) MARCKS 213002_at I 2 Suicide in 6 myristoylated alanine- Depression (I) rich protein kinase C (Pandey, G. N. et substrate al. 2003) PGLS 230699_at I 2 Suicide 6 6-Phosphogluconolactonase PFC (D) (Kekesi K. A. et al. 2012) PTEN 222176_at I 2 Suicide 6 phosphatase and tensin PFC, HIP (I) homolog (Dwivedi Y. et al. 2010) RECK 216153_x_at I 2 Suicide 6 reversion-inducing- (I) PFC (Sequeira cysteine-rich protein with A. et al. 2012) kazal motifs SPTBN1 200671_s_at D 2 Suicide in mood 6 spectrin, beta, non- disorders erythrocytic 1 (I) NAC (Sequeira A. et al. 2012) TNFSF10 202688_at I 2 Suicide in 6 tumor necrosis factor 202687_s_at Schizophrenia (ligand) superfamily, 214329_x_at (I)PFC (Kim, S. member 10 et al. 2007) Suicide in Depression (I) PFC (Zhurov V. et al. 2012) ABCA1 203504_s_at I 4 4 ATP-binding cassette, sub-family A (ABC1), member 1 ARHGEF40 241631_at I 4 4 (FLJ10357) Rho guanine nucleotide exchange factor (GEF) 40 CASC1 220168_at I 4 4 cancer susceptibility candidate 1 DHRS9 219799_s_at I 4 4 dehydrogenase/reductase (SDR family) member 9 DISC1 244642_at I 2 (Assoc) 4 disrupted in Suicide schizophrenia 1 (Galfalvy H. et al. 2011) EIF2AK2 204211_x_at I 4 4 eukaryotic translation initiation factor 2-alpha kinase 2 LOC727820 231247_s_at I 4 4 uncharacterized LOC727820 LOC727820 MAP3K3 242117_at I 4 4 mitogen-activated protein kinase kinase kinase 3 MBNL2 205017_s_at D 2 (Assoc) 4 muscleblind-like 2 Suicide (Drosophila) (Galfalvy H. et al. 2011) MT-ND6 (ND6) 1553575_at I 4 4 mitochondrially encoded NADH dehydrogenase 6 OR2J3 217334_at D 4 4 olfactory receptor, family 2, subfamily J, member 3 RBM47 1565597_at I 4 4 RNA binding motif protein 47 RHEB 227633_at D 2 (Assoc) 4 Ras homolog enriched in Suicide brain (Menke A. et al. 2012) RICTOR 228248_at I 4 4 RPTOR independent companion of MTOR, complex 2 SAMD9L 243271_at; I 4 4 sterile alpha motif 230036_at domain containing 9-like

In order to validate the Discovery Cohort findings in the most stringent way possible, SAT1 levels in blood samples from the Validation Cohort of 9 consecutive male suicide completers obtained from the coroner's office were evaluated. SAT1 gene expression levels were found to be elevated in 9 out of 9 (100%) subjects who committed suicide. In each suicide completer, the increase in SAT1 was at least three standard deviations above the average levels in high suicidal ideation subjects. The results were further strengthened by using a panel of the two markers (SAT1 and CD24) (FIGS. 3A-C). As shown in FIGS. 3A-3C, risk marker SAT1 expression was significantly increased (p=0.0057) between subjects with high suicidal ideation (SI) (mean=3413.37) and those reporting no suicidal ideation (mean=2642.97). In the Validation Cohort of suicide completers (mean=7171.51), a significantly greater expression of SAT1 was found as compared to both high suicide ideation (p=7.27e-07) and no suicide ideation (p=1.51e-07) groups from the Discovery Cohort (FIG. 3A). Further, suicide risk score was calculated by scoring the standard deviation band a subject fell within as derived from the high suicide ideation Discovery Cohort, starting from the mean of the high suicide ideation Discovery Cohort (FIG. 3B). 0 indicates the subject fell between the means of the high and low suicide ideation subjects in the Discovery Cohort. A score of 1 means between the mean of the high suicide ideation and the first standard deviation above it, score of 2 between the first and second standard deviation, score of 3 between the second and third standard deviation, and so on.

As shown in FIGS. 3D-3F, protective marker CD 24 expression was significantly decreased (p=0.0044) within the Discovery Cohort between subjects reporting high suicide ideation (mean=73.01) and no suicide ideation (mean=108.634). The Validation Cohort of suicide completers (mean=71.61) was also significantly decreased (p=0.0031) when compared to subjects reporting no suicide ideation (FIG. 3D). Suicide risk score was defined as the standard deviation band in which the subject expression fell below the mean of the high suicide ideation Discovery Cohort (FIG. 3E).

FIG. 3G shows the sum of standard deviation suicide risk scores for both biomarkers (SAT1 and CD24) in the Validation Cohort (i.e., suicide completers).

One of the other biomarkers identified to be decreased in high suicidal states in the current Example was the circadian clock gene DBP (D-box binding protein). Serendipitously, previous work showed that mice engineered to lack DBP were stress-reactive and displayed a behavioral phenotype similar to bipolar disorder and co-morbid alcoholism (Le-Niculescu H. et al., “Phenomic, convergent functional genomic, and biomarker studies in a stress-reactive genetic animal model of bipolar disorder and co-morbid alcoholism,” American Journal of Medical Genetics, Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics 147B, 134-166, doi:10.1002/ajmg.b.30707 (2008)). In addition to bipolar disorder, alcoholism is known to increase the risk for suicide. Treatment with omega-3 fatty acids normalized the phenotype of those mice. Low omega-3 levels have been previously correlated with increased suicidality in human epidemiological studies (see Sublette M. et al., “Omega-3 polyunsaturated essential fatty acid status as a predictor of future suicide risk,” Am J Psychiatry 163, 1100-1102, doi:10.1176/appi.ajp.163.6.110 (2006); Lewis M. D. et al., “Suicide deaths of active-duty US military and omega-3 fatty-acid status: a case-control comparison,” J Clin Psychiatry 72, 1585-1590, doi:10.4088/JCP.11m06879 (2011)). Pathway analyses of the instant suicidality biomarker data identified among the top pathways the omega-3 docosahexaenoic acid (DHA) signaling pathway. Several of the biomarkers from this Example (those bolded in Table 6 in “Modulated by DHA” column)) were changed in expression by omega-3 treatment in the blood of the DBP mouse model in opposite direction to our human suicidality data (Table 6). PTEN, a biomarker increased in suicidality in the current Example in the blood, as well as in the brain of suicide completers, was also increased in the amygdala and decreased in the pre-frontal cortex of DBP knock-out mice subjected to stress.

TABLE 6 Genes in our dataset modulated by Clozapine and Omega-3 Fatty Acids (DHA). Direction Gene of CFG Modulated by Modulated by Symbol Gene Name Change score Clozapine DHA SAT1 spermidine/spermine N1- I 8 (D) Blood acetyl transferase 1 GBP1 guanylate nucleotide binding I 8 (D) Blood protein 1 ATP13A2 ATPase type 13A2 D 6 (D) VT EPHX1 epoxide hydrolase 1, D 6 (D) VT microsomal IL1B interleukin 1 beta I 6 (I) Blood (D) Blood LHFP lipoma HMGIC fusion I 6 (I) Blood, VT (D) Blood partner MARCKS myristoylated alanine rich I 6 (I) HIP protein kinase C substrate PTEN phosphatase and tensin I 6 (I) VT homolog SPTBN1 spectrin, beta, non- D 6 (I) Blood, VT (D) Blood erythrocytic 1 ABCA1 ATP-binding cassette, sub- I 4 (I) VT family A (ABC1), member 1 MAP3K3 mitogen-activated protein I 4 (D) Blood kinase kinase kinase 3 MBNL2 muscleblind-like 2 D 4 (I) Blood (D) Blood ATG3 autophagy-related 3 (yeast) I 2 (D) Blood ATXN2 ataxin 2 I 2 (I) VT CCR1 chemokine (C-C motif) I 2 (D) Blood receptor 1 CCRN4L CCR4 carbon catabolite I 2 (I) Blood repression 4-like CD84 CD84 antigen D 2 (I) Blood (D) Blood CEACAM1 CEA-related cell adhesion I 2 (D) Blood molecule 1 CELA1 chymotrypsin-like elastase D 2 (D) Blood family, member 1 CLEC4E C-type lectin domain family I 2 (D) Blood 4, member e CLEC7A C-type lectin domain family I 2 (D) Blood 7, member a CORO1C coronin, actin binding protein I 2 (D) VT 1C DLGAP1 discs, large (Drosophila) I 2 (I) VT homolog-associated protein 1 DOCK1 dedicator of cytokinesis 1 D 2 (D) VT DOCK4 dedicator of cytokinesis 4 I 2 (D) HIP FABP3 fatty acid binding protein 3, I 2 (I) VT muscle and heart FNIP1 folliculin interacting protein 1 I 2 (I) VT FOXK2 forkhead box K2 D 2 (I) VT FZR1 fizzy/cell division cycle 20 D 2 (I) Blood related 1 (

) GBP2 guanylate nucleotide binding I 2 (D) VT protein 2 GREM1 gremlin 1 I 2 (D) HIP IFIT2 interferon-induced protein I 2 (I) Blood (D) Blood with tetratricopeptide repeats 2 IFIT3 interferon-induced protein with I 2 (D) NAC tetratricopeptide repeats 3 IL1RAP interleukin 1 receptor accessory I 2 (I) VT protein KLHDC3 kelch domain containing 3 D 2 (I) VT KPNA3 karyopherin (importin) alpha 3 I 2 (D) VT LARP4 La ribonucleoprotein domain D 2 (I) VT family, member 4 LONRF1 LON peptidase N-terminal I 2 (I) VT domain and ring finger 1 MCTP1 multiple C2 domains, I 2 (I) HIP transmembrane 1 MDM4 transformed mouse 3T3 cell I 2 (D) Blood double minute 4 NUB1 negative regulator of ubiquitin- I 2 (D) VT like proteins 1 NUDT3 nudix (nucleotide I 2 (D) Blood diphosphate linked moiety X)-type motif 3 OGT O-linked N-acetylglucosamine I 2 (I) Blood (D) HIP; (I) (GlcNAc) transferase NAC PELI1 pellino 1 I 2 (I) AMY (I) HIP PKN2 protein kinase N2 I 2 (I) Blood R3HDM1 R3H domain 1 (binds single- I 2 (I) VT (D) Blood stranded nucleic acids) RAI14 retinoic acid induced 14 D 2 (I) HIP RASSF3 Ras association (RaIGDS/AF- I 2 (I) VT (D) Blood 6) domain family member 3 RPL37A ribosomal protein L37a I 2 (I) Blood RPLP2 ribosomal protein, large P2 I 2 (I) Blood RSAD2 radical S-adenosyl methionine I 2 (I) Blood (I) Blood domain containing 2 S100A8 S100 calcium binding protein I 2 (D) Blood (D) Blood A8 (calgranulin A) SFRP2 secreted frizzled-related protein D 2 (D) VT 2 SLC25A37 solute carrier family 25, I 2 (I) VT (I) Blood member 37 SLC2A13 solute carrier family 2 I 2 (I) VT (facilitated glucose transporter), member 13 SPOCK2 sparc/osteonectin, cwcv and D 2 (I) VT kazal-like domains proteoglycan 2 TAOK1 TAO kinase 1 I 2 (I) VT (D) PFC; (I) HIP TB1X transducin (beta)-like 1 X- I 2 (D) VT linked TCEA1 transcription elongation factor I 2 (D) VT (I) Blood A (SII) 1 TMEM140 transmembrane protein 140 I 2 (I) Blood (D) Blood TMEM154 transmembrane protein 154 I 2 (D) Blood TNFAIP6 tumor necrosis factor alpha I 2 (I) AMY induced protein 6 TNK2 tyrosine kinase, non-receptor, 2 D 2 (D) VT TOP1 topoisomerase (DNA) I I 2 (I) VT (I) Blood TRIP12 thyroid hormone receptor I 2 (I) VT interactor 12 TRPM7 transient receptor potential I 2 (D) AMY cation channel, subfamily M, member 7 UBE2B ubiquitin-conjugating enzyme I 2 (I) Blood, (I) Blood E2B, RAD6 homology (S. AMY, PFC cerevisiae) WDR77 WD repeat domain 77 D 2 (D) VT Bold are genes that are changed in opposite direction to suicidal ideation by one or both of the treatments.

Other circadian clock-modulated genes identified as biomarkers for suicidality were PIK3R5, MARCKS, IL1B, CASC1, CCRN4L, H3F3B, RBCK1, TNK2, and UBE2B. Additionally, biomarkers, as bolded in Table 6 in the “Modulated by Clozapine” column, provided evidence for modulation by clozapine in blood in opposite direction to the human suicidality data in previous independent animal model pharmacogenomics studies (Table 6). Clozapine is the only FDA approved treatment for suicidality. Thus, the convergent evidence for the instant biomarkers is strong in translational ways beyond those used for their discovery and selection. S100A8 may be a key biomarker to monitor in terms of response to treatment with classic (clozapine) and complementary (omega-3) agents. Other potential drugs to be studied for modulating suicidality were revealed by the above analyses (Tables 4 and 6).

SAT1, FOXN3, DISC1, MBNL2 and RHEB had genetic association evidence for suicidality, suggesting that they are not only state biomarkers but also trait factors influencing suicidal risk. DISC1 is also one of the top candidate genes for schizophrenia based on a large scale CFG analysis of schizophrenia GWAS recently conducted (Ayalew M. et al., “Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction,” Molecular Psychiatry 17, 887-905, doi:10.1038/mp.2012.37 (2012)), while DISC1 and MBNL2 are also among the top candidate genes for bipolar disorder based on a large scale CFG analysis of bipolar disorder GWAS (Patel S. D. et al., “Coming to grips with complex disorders: genetic risk prediction in bipolar disorder using panels of genes identified through convergence functional genomics,” American Journal of Medical Genetics Part b, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric genetics 153B, 850-877, doi:10.1pp2/ajmg.b.31087 (2010)). Additionally, DISC1 has clear animal model data for the role of its interaction with environmental stress in the pathophysiology of psychotic depression. DISC1 and MBNL2 may thus be key state and trait factors for suicidality risk in psychotic mood disorder subjects, and an indication for clozapine treatment in such subjects.

Suicide biomarkers that were identified in this study were overlapped with biomarkers identified as mood biomarkers (Le-Niculescu H. et al., “Identifying blood biomarkers for mood disorders using convergent functional genomics,” Molecular Psychiatry 14, 156-174, doi:10.1111/ele.12064 (2009)) and psychosis biomarkers (Kurian S. M. et al., “Identification of blood biomarkers for psychosis using convergent functional genomics,” Molecular Psychiatry 16, 37-58, doi:10.1038/mp.2009.117 (2011)) (Table 7). DOCK5 and 4 other biomarkers (as bolded in Table 7 in the “Direction of change in Mood” column were changed in high suicidal states in opposite direction to their change in high mood states, and DOCK5 and 6 other biomarkers (as bolded in Table 7 in the “Direction of change in Hallucination” or “Direction of change in Delusions” columns) were changed in the same direction as their change in high psychosis states, suggesting that suicidality can indeed be viewed as a psychotic depressed state, and that DOCK5 may be an additional key biomarker reflecting that state.

TABLE 7 Genes with evidence as mood and/or psychosis blood biomarkers. CFG Direction of Direction of Direction of Direction of score in change in change in change in change in Gene Symbol Gene Name SI SI Mood Hallucination Delusions LEPR leptin receptor 6 D (I) CD84 CD84 molecule 2 D (I) DOCK5 dedicator of 2 I (D) (I) cytokinesis 5 EPM2A epilepsy, 2 D (D) progressive myoclonus type 2A, Lafora disease (laforin) ERICH1 glutamate-rich 1 2 I (D) (D) FKBP7 FK506 binding 2 D (D) protein 7 IDH1 isocitrate 2 I (D) dehydrogenase 1 (NADP+), soluble KIAA0494 KIAA0494 2 I (D) LARP4 La 2 D (D) ribonucleoprotein domain family, member 4 MXD1 MAX dimerization 2 I (I) protein 1 PID1 phosphotyrosine 2 D (D) interaction domain containing 1 PML promyelocytic 2 I (I) leukemia PPP2R1B protein 2 D (D) phosphatase 2, regulatory subunit A, beta SLC2A13 solute carrier 2 I (D) family 2 (facilitated glucose transporter), member 13 TRIM6 tripartite motif 2 I (D) containing 6 TRPM7 transient receptor 2 I (I) potential cation channel, subfamily M, member 7 Discussion

This Example shows overlap at a gene and pathway level with cancer and apoptosis (Table 3, Table 8). SAT1, for example, is a key catabolic enzyme for polyamines Polyamine levels within cells control cell viability, and significant decreases in polyamine levels can result in apoptosis. They appear to reflect an endowment for cellular and organismal activity and growth, key characteristics of mood. SAT1, which increased in suicidal subjects of this Example, is highly inducible by a variety of stimuli, including toxins, cytokines, heat shock, ischemia, and other stresses.

TABLE 8 Complete list of genes differentially expressed in the discovery cohort overlapping between the intra-subject and inter-subject analyses (n = 246). Evidence or possible roles in Probe set ID Gene Symbol Gene Name Change Total CFG Score apoptosis 203455_s_at SAT1 spermidine/spermine N1- I 8 yes acetyltransferase 1 209772_s_at CD24 CD24 molecule D 8 yes 230790_x_at FOXN3 forkhead box N3 I 8 231577_s_at; GBP1 guanylate binding protein 1, I 8 yes 202269_x_at; interferon-inducible 202270_at 227553_at PIK3R5 phosphoinositide-3-kinase, I 8 regulatory subunit 5 221653_x_at APOL2 apolipoprotein L, 2 I 6 218608_at ATP13A2 ATPase type 13A2 D 6 214149_s_at ATP6V0E1 ATPase, H+ transporting, I 6 lysosomal 9 kDa, V0 subunit e1 202017_at EPHX1 epoxide hydrolase 1, microsomal D 6 yes (xenobiotic) 239099_at GCOM1 GRINL1A complex locus 1 I 6 201185_at HTRA1 HtrA serine peptidase 1 D 6 yes 39402_at IL1B interleukin 1, beta I 6 yes 211354_s_at LEPR leptin receptor D 6 yes 218656_s_at LHFP lipoma HMGIC fusion partner I 6 236156_at LIPA lipase A, lysosomal acid, I 6 cholesterol esterase 213002_at MARCKS myristoylated alanine-rich I 6 yes protein kinase C substrate 230699_at PGLS 6-phosphogluconolactonase I 6 222176_at PTEN phosphatase and tensin homolog I 6 yes 216153_x_at RECK reversion-inducing-cysteine-rich I 6 yes protein with kazal motifs 200671_s_at SPTBN1 spectrin, beta, non-erythrocytic 1 D 6 yes 202688_at TNFSF10 tumor necrosis factor (ligand) I 6 yes superfamily, member 10 203504_s_at; ABCA1 ATP-binding cassette, sub-family I 4 203505_at A (ABC1), member 1 241631_at ARHGEF40 Rho guanine nucleotide exchange I 4 factor (GEF) 40 220168_at CASC1 cancer susceptibility candidate 1 I 4 219799_s_at DHRS9 dehydrogenase/reductase (SDR I 4 family) member 9 244642_at DISC1 disrupted in schizophrenia 1 I 4 204211_x_at EIF2AK2 eukaryotic translation initiation I 4 yes factor 2-alpha kinase 2 231247_s_at LOC727820 uncharacterized LOC727820 I 4 242117_at MAP3K3 mitogen-activated protein kinase I 4 yes kinase kinase 3 205017_s_at MBNL2 muscleblind-like splicing D 4 regulator 2 1553575_at MT-ND6 mitochondrially encoded NADH I 4 dehydrogenase 6 217334_at OR2J3 olfactory receptor, family 2, D 4 subfamily J, member 3 1565597_at RBM47 RNA binding motif protein 47 I 4 227633_at RHEB Ras homolog enriched in brain D 4 yes 228248_at RICTOR RPTOR independent companion I 4 of MTOR, complex 2 243271_at; SAMD9L sterile alpha motif domain I 4 230036_at containing 9-like 206995_x_at SCARF1 scavenger receptor class F, I 4 member 1 213119_at SLC36A1 solute carrier family 36 I 4 (proton/amino acid symporter), member 1 232375_at STAT1 signal transducer and activator of I 4 yes transcription 1, 91 kDa 236879_at UBA6 ubiquitin-like modifier activating I 4 enzyme 6 1563075_s_at ZC3HAV1 zinc finger CCCH-type, antiviral I 4 1 213736_at COX5B cytochrome c oxidase subunit Vb I 3 203874_s_at SMARCA1 SWI/SNF related, matrix I 3 associated, actin dependent regulator of chromatin, subfamily a, member 1 229577_at AGPAT6 1-acylglycerol-3-phosphate O- D 2 acyltransferase 6 (lysophosphatidic acid acyltransferase, zeta) 206513_at AIM2 absent in melanoma 2 I 2 yes 227438_at ALPK1 alpha-kinase 1 I 2 210873_x_at APOBEC3A apolipoprotein B mRNA editing I 2 enzyme, catalytic polypeptide-like 3A 239002_at ASPM asp (abnormal spindle) homolog, D 2 microcephaly associated (Drosophila) 222840_at ATG2B autophagy related 2B D 2 220237_at ATG3 autophagy related 3 I 2 211852_s_at ATRN attractin D 2 243839_s_at ATXN2 ataxin 2 I 2 yes 204516_at ATXN7 ataxin 7 I 2 203140_at; BCL6 B-cell CLL/lymphoma 6 I 2 yes 228758_at 219072_at BCL7C B-cell CLL/lymphoma 7C D 2 214068_at BEAN1 brain expressed, associated with D 2 NEDD4, 1 212563_at BOP1 block of proliferation 1 D 2 233809_at C15orf63 chromosome 15 open reading I 2 yes frame 63 221954_at C20orf111 chromosome 20 open reading I 2 yes frame 111 1564276_at C5orf56 chromosome 5 open reading I 2 frame 56 1553329_at C7orf45 chromosome 7 open reading I 2 frame 45 227364_at CAPZA1 capping protein (actin filament) I 2 yes muscle Z-line, alpha 1 213596_at CASP4 caspase 4, apoptosis-related I 2 yes cysteine peptidase 207500_at CASP5 caspase 5, apoptosis-related I 2 yes cysteine peptidase 205099_s_at CCR1 chemokine (C-C motif) receptor I 2 yes 1 1554283_at CCRN4L CCR4 carbon catabolite I 2 repression 4-like (S. cerevisiae) 206485_at CD5 CD5 molecule D 2 yes 243931_at CD58 CD58 molecule I 2 yes 211189_x_at CD84 CD84 molecule D 2 234255_at CDC42SE2 CDC42 small effector 2 I 2 209498_at CEACAM1 carcinoembryonic antigen-related I 2 yes cell adhesion molecule 1 (biliary glycoprotein) 224198_at CELA1 chymotrypsin-like elastase D 2 family, member 1 210069_at CHKB-CPT1B CHKB-CPT1B readthrough I 2 (non-protein coding) 222174_at CHURC1- CHURC1-FNTB readthrough D 2 FNTB 209571_at CIR1 corepressor interacting with I 2 RBPJ, 1 219859_at CLEC4E C-type lectin domain family 4, I 2 member E 221698_s_at CLEC7A C-type lectin domain family 7, I 2 member A yes 200861_at CNOT1 CCR4-NOT transcription D 2 complex, subunit 1 211141_s_at CNOT3 CCR4-NOT transcription D 2 complex, subunit 3 1569703_a_at CORO1C coronin, actin binding protein, 1C I 2 yes 205624_at CPA3 carboxypeptidase A3 (mast cell) I 2 203532_x_at CUL5 cullin 5 D 2 yes 202434_s_at CYP1B1 cytochrome P450, family 1, D 2 yes subfamily B, polypeptide 1 208281_x_at DAZ1 deleted in azoospermia 1 I 2 209782_s_at DBP D site of albumin promoter D 2 yes (albumin D-box) binding protein 218943_s_at DDX58 DEAD box polypeptide 58 I 2 yes 240358_at DENND3 DENN/MADD domain I 2 containing 3 1556769_a_at DLGAP1 discs, large (Drosophila) I 2 homolog-associated protein 1 233052_at DNAH8 dynein, axonemal, heavy chain 8 D 2 yes 223371_s_at DNAJC4 DnaJ (Hsp40) homolog, D 2 subfamily C, member 4 237311_at DOCK1 dedicator of cytokinesis 1 D 2 yes 244840_x_at DOCK4 dedicator of cytokinesis 4 I 2 230207_s_at DOCK5 dedicator of cytokinesis 5 I 2 225415_at DTX3L deltex 3-like (Drosophila) I 2 210525_x_at EFCAB11 EF-hand calcium binding domain I 2 11 214313_s_at EIF5B eukaryotic translation initiation I 2 factor 5B 224727_at EMC10 ER membrane protein complex D 2 subunit 10 217245_at EPAG early lymphoid activation protein D 2 220874_at EPB41 erythrocyte membrane protein I 2 band 4.1 (elliptocytosis 1, RH- linked) 210870_s_at EPM2A epilepsy, progressive myoclonus D 2 yes type 2A, Lafora disease (laforin) 239979_at EPSTI1 epithelial stromal interaction 1 I 2 (breast) 1570371_a_at EPT1 ethanolaminephosphotransferase D 2 1 (CDP-ethanolamine-specific) 227016_at ERICH1 glutamate-rich 1 I 2 225764_at ETV6 ets variant 6 I 2 yes 214285_at FABP3 fatty acid binding protein 3, I 2 muscle and heart (mammary- derived growth inhibitor) 1557385_at FAM161A family with sequence similarity D 2 161, member A 229543_at FAM26F family with sequence similarity I 2 26, member F 216950_s_at FCGR1A Fc fragment of IgG, high affinity I 2 yes Ia, receptor (CD64) 1554360_at; FCHSD2 FCH and double SH3 domains 2 I 2 231302_at 1553906_s_at FGD2 FYVE, RhoGEF and PH domain I 2 yes containing 2 224002_s_at FKBP7 FK506 binding protein 7 D 2 211454_x_at; FKSG49 FKSG49 I 2 224288_x_at 226419_s_at FLJ44342 uncharacterized LOC645460 I 2 228768_at FNIP1 folliculin interacting protein 1 I 2 1556667_at FONG uncharacterized LOC348751 D 2 242938_s_at FOXK2 forkhead box K2 D 2 230645_at FRMD3 FERM domain containing 3 I 2 230744_at FSTL1 follistatin-like 1 D 2 1563509_at; FYB FYN binding protein I 2 224148_at 209416_s_at FZR1 fizzy/cell division cycle 20 D 2 yes related 1 (Drosophila) 202748_at; GBP2 guanylate binding protein 2, I 2 242907_at interferon-inducible 229625_at GBP5 guanylate binding protein 5 I 2 211060_x_at GPAA1 glycosylphosphatidylinositol D 2 anchor attachment protein 1 homolog (yeast) 237690_at GPR115 G protein-coupled receptor 115 I 2 218468_s_at GREM1 gremlin 1 I 2 yes 235957_at GRIP1 glutamate receptor interacting I 2 protein 1 213826_s_at H3F3B H3 histone, family 3B (H3.3B) I 2 205221_at HGD homogentisate 1,2-dioxygenase I 2 227614_at HKDC1 hexokinase domain containing 1 D 2 210747_at HLA-DQB1 major histocompatibility I 2 yes complex, class II, DQ beta 1 242001_at IDH1 isocitrate dehydrogenase 1 I 2 (NADP+), soluble 226757_at IFIT2 interferon-induced protein with I 2 yes tetratricopeptide repeats 2 229450_at IFIT3 interferon-induced protein with I 2 tetratricopeptide repeats 3 230128_at IGLL5 immunoglobulin lambda-like I 2 polypeptide 5 225025_at IGSF8 immunoglobulin superfamily, D 2 member 8 1562468_at IL1RAP interleukin 1 receptor accessory I 2 yes protein 207688_s_at INHBC inhibin, beta C I 2 yes 238725_at IRF1 interferon regulatory factor 1 I 2 yes 210119_at; KCNJ15 potassium inwardly-rectifying I 2 216782_at channel, subfamily J, member 15 231513_at; KCNJ2 potassium inwardly-rectifying I 2 206765_at channel, subfamily J, member 2 1559023_a_at KIAA0494 KIAA0494 I 2 225193_at KIAA1967 KIAA1967 D 2 yes 208784_s_at KLHDC3 kelch domain containing 3 D 2 1565690_at KPNA3 karyopherin alpha 3 (importin I 2 alpha 4) 208974_x_at KPNB1 karyopherin (importin) beta 1 I 2 yes 1555384_a_at LARP4 La ribonucleoprotein domain D 2 family, member 4 215229_at LOC100129973 uncharacterized LOC100129973 D 2 1569746_s_at LOC100505783 uncharacterized LOC100505783 I 2 215322_at LONRF1 LON peptidase N-terminal I 2 domain and ring finger 1 233818_at LTN1 listerin E3 ubiquitin protein I 2 ligase 1 232283_at LYSMD1 LysM, putative peptidoglycan- I 2 binding, domain containing 1 215902_at MARCH 6 membrane-associated ring finger I 2 (C3HC4) 6, E3 ubiquitin protein ligase 1554730_at MCTP1 multiple C2 domains, I 2 transmembrane 1 235589_s_at MDM4 Mdm4 p53 binding protein I 2 yes homolog (mouse) 222567_s_at MEX3C mex-3 homolog C (C. elegans) D 2 241541_at MIB2 mindbomb E3 ubiquitin protein I 2 ligase 2 225826_at MMAB methylmalonic aciduria D 2 (cobalamin deficiency) cblB type 239273_s_at MMP28 matrix metallopeptidase 28 D 2 yes 221995_s_at MRP63 mitochondrial ribosomal protein I 2 63 228846_at MXD1 MAX dimerization protein 1 I 2 yes 211010_s_at NCR3 natural cytotoxicity triggering D 2 yes receptor 3 243357_at NEGR1 neuronal growth regulator 1 D 2 223218_s_at NFKBIZ nuclear factor of kappa light I 2 yes polypeptide gene enhancer in B- cells inhibitor, zeta 214101_s_at NPEPPS aminopeptidase puromycin I 2 sensitive 1557071_s_at NUB1 negative regulator of ubiquitin- I 2 yes like proteins 1 1561847_at NUDT17 nudix (nucleoside diphosphate D 2 linked moiety X)-type motif 17 1569990_at NUDT3 nudix (nucleoside diphosphate I 2 linked moiety X)-type motif 3 243934_at ODF3B outer dense fiber of sperm tails I 2 3B 229787_s_at OGT O-linked N-acetylglucosamine I 2 yes (GlcNAc) transferase 1569617_at OSBP2 oxysterol binding protein 2 D 2 243287_s_at OSTM1 osteopetrosis associated I 2 transmembrane protein 1 231838_at PABPC1L poly(A) binding protein, I 2 cytoplasmic 1-like 235157_at PARP14 poly (ADP-ribose) polymerase I 2 family, member 14 227807_at PARP9 poly (ADP-ribose) polymerase I 2 family, member 9 241956_at PCGF5 polycomb group ring finger 5 I 2 222045_s_at PCIF1 PDX1 C-terminal inhibiting D 2 factor 1 217695_x_at PELI1 pellino E3 ubiquitin protein I 2 ligase 1 225958_at PHC1 polyhomeotic homolog 1 I 2 (Drosophila) 237867_s_at PID1 phosphotyrosine interaction D 2 domain containing 1 216112_at PKN2 protein kinase N2 I 2 yes 241916_at PLSCR1 phospholipid scramblase 1 I 2 yes 235508_at PML promyelocytic leukemia I 2 yes 202884_s_at PPP2R1B protein phosphatase 2, regulatory D 2 yes subunit A, beta 1559119_at PPP6R3 protein phosphatase 6, regulatory I 2 subunit 3 221270_s_at QTRT1 queuine tRNA-ribosyltransferase D 2 1 241320_at R3HDM1 R3H domain containing 1 I 2 1553285_s_at RAD9B RAD9 homolog B (S. pombe) I 2 202052_s_at RAI14 retinoic acid induced 14 D 2 yes 230466_s_at RASSF3 Ras association (RalGDS/AF-6) I 2 domain family member 3 204927_at RASSF7 Ras association (RalGDS/AF-6) D 2 domain family (N-terminal) member 7 237626_at RB1CC1 RB1-inducible coiled-coil 1 I 2 yes 232150_at RBCK1 RanBP-type and C3HC4-type I 2 yes zinc finger containing 1 1560340_s_at RP9P retinitis pigmentosa 9 I 2 pseudogene 214041_x_at RPL37A ribosomal protein L37a I 2 200908_s_at RPLP2 ribosomal protein, large, P2 I 2 242625_at RSAD2 radical S-adenosyl methionine I 2 domain containing 2 214370_at S100A8 S100 calcium binding protein A8 I 2 yes 242190_at SDAD1 SDA1 domain containing 1 I 2 214257_s_at SEC22B SEC22 vesicle trafficking protein I 2 homolog B (S. cerevisiae) (gene/pseudogene) 223121_s_at SFRP2 secreted frizzled-related protein 2 D 2 yes 35626_at SGSH N-sulfoglucosamine D 2 sulfohydrolase 228527_s_at SLC25A37 solute carrier family 25 I 2 (mitochondrial iron transporter), member 37 234268_at SLC2A13 solute carrier family 2 (facilitated I 2 glucose transporter), member 13 235536_at SNORD89 small nucleolar RNA, C/D box I 2 89 208012_x_at; SP110 SP110 nuclear body protein I 2 209762_x_at 228975_at SP6 Sp6 transcription factor D 2 1557593_at SPAG17 sperm associated antigen 17 D 2 202523_s_at SPOCK2 sparc/osteonectin, cwcv and D 2 kazal-like domains proteoglycan (testican) 2 243522_at SPPL3 signal peptide peptidase like 3 I 2 213562_s_at SQLE squalene epoxidase D 2 219055_at SRBD1 S1 RNA binding domain 1 I 2 1565566_a_at STX7 syntaxin 7 I 2 1557305_at TACC1 transforming, acidic coiled-coil I 2 containing protein 1 216226_at TAF4B TAF4b RNA polymerase II, D 2 TATA box binding protein (TBP)-associated factor, 105 kDa 231193_s_at TAOK1 TAO kinase 1 I 2 yes 225973_at TAP2 transporter 2, ATP-binding I 2 cassette, sub-family B (MDR/TAP) 221398_at TAS2R8 taste receptor, type 2, member 8 I 2 213401_s_at TBL1X transducin (beta)-like 1X-linked I 2 1566208_at TCEA1 transcription elongation factor A I 2 (SII), 1 1552804_a_at TIRAP toll-interleukin 1 receptor (TIR) D 2 yes domain containing adaptor protein 224321_at TMEFF2 transmembrane protein with I 2 EGF-like and two follistatin-like domains 2 235159_at TMEM140; transmembrane protein 140 I 2 243465_at 238063_at TMEM154 transmembrane protein 154 I 2 227386_s_at TMEM200B transmembrane protein 200B I 2 1554206_at TMLHE trimethyllysine hydroxylase, I 2 epsilon 206025_s_at; TNFAIP6 tumor necrosis factor, alpha- I 2 206026_s_at induced protein 6 1555557_a_at TNK2 tyrosine kinase, non-receptor, 2 D 2 1558354_s_at TOP1 topoisomerase (DNA) I I 2 yes 231978_at TPCN2 two pore segment channel 2 I 2 223599_at TRIM6 tripartite motif containing 6 I 2 242688_at TRIP12 thyroid hormone receptor I 2 interactor 12 1565887_at TRPM7 transient receptor potential cation I 2 yes channel, subfamily M, member 7 215107_s_at TTC22 tetratricopeptide repeat domain D 2 22 202476_s_at TUBGCP2 tubulin, gamma complex D 2 yes associated protein 2 228588_s_at UBE2B ubiquitin-conjugating enzyme I 2 yes E2B 1568903_at UBR5 ubiquitin protein ligase E3 I 2 yes component n-recognin 5 205586_x_at VGF VGF nerve growth factor D 2 inducible 242390_at WDFY1 WD repeat and FYVE domain I 2 containing 1 201421_s_at WDR77 WD repeat domain 77 D 2 1569428_at WIBG within bgcn homolog D 2 yes (Drosophila) 213734_at WSB2 WD repeat and SOCS box I 2 containing 2 228617_at XAF1 XIAP associated factor 1 I 2 yes 1554037_a_at ZBTB24 zinc finger and BTB domain D 2 containing 24 219062_s_at ZCCHC2 zinc finger, CCHC domain I 2 containing 2 1555982_at ZFYVE16 zinc finger, FYVE domain I 2 containing 16 228864_at ZNF653 zinc finger protein 653 D 2

CD24, the top biomarker decreased in suicidal subjects of this Example, also has roles in apoptosis. Mice lacking CD24 show an increased rate of apoptosis (Duckworth C. A. et al., “CD24 is expressed in gastric parietal cells and regulates apoptosis and the response to Helicobacter felis infection in the murine stomach,” American Journal of Physiology, Gastrointestinal and Liver Physiology 303, G915-926, doi:10.1152/ajpgi.00068.2012 (2012)). It could be that simpler mechanisms related to cellular survival and programed cell-death decision have been recruited by evolution for higher mental functions such as thoughts and behaviors leading to suicidality. In that sense, suicidality could be viewed as whole-organism self-poptosis. Interestingly, lithium, a medication with clinical evidence for preventing suicidality in bipolar disorder, has anti-apoptotic effects at a cellular level. Imaging studies have shown reduced gray matter volume in the brain of individuals with bipolar disorder and history of suicide attempts. Long-term lithium treatment was associated with increased gray matter volumes in the same areas where suicide was associated with decreased gray matter.

Taken together, the results of this Example have implications for the understanding of suicide, as well as for the development of objective laboratory tests and tools to diagnose and track suicidal risk and to monitor response to treatment.

More particularly, it was found that suicidality may be associated with dysphoric mood, as well as increased psychosis, anxiety and stress. SAT1 blood gene expression levels, in particular, showed a trend towards increase in low mood, high psychosis, high anxiety, and high stress in the bipolar subjects (see FIGS. 4A-4F).

Example 2

In this Example, SAT1 was validated by analyzing subsequent hospitalizations with and without suicidality and to previous hospitalizations with and without suicidality in two live follow-up cohorts, one bipolar (n=42) and one psychosis (schizophrenia/schizoaffective; n=46).

Particularly, the bipolar follow-up cohort (Table 9A) consisted of male Caucasian subjects in which whole-genome blood gene expression data, including levels of SAT1, were obtained at the testing visits as described in Example 1. If the subjects had multiple testing visits, the visit with the highest SAT1 level was selected for this analysis. The subjects' subsequent number of hospitalizations with or without suicidality was tabulated from electronic medical records.

The psychosis (schizophrenia/schizoaffective) follow-up cohort (n=46) (Table 9B) similarly consisted of Caucasian subjects in which whole-genome blood gene expression data, including levels of SAT1, were obtained at testing visits as described for the bipolar follow-up cohort. If the subjects had multiple testing visits, the visit with the highest SAT1 level was selected for this analysis. The subjects' subsequent number of hospitalizations with or without suicidality was tabulated from electronic medical records. A hospitalization was deemed to be without suicidality if suicidality was not listed as a reason for admission, and no suicidal ideation was described in the admission and discharge medical notes. Conversely, a hospitalization was deemed to be due to suicidality if suicidal acts or intent was listed as a reason for admission, and suicidal ideation was described in the admission and discharge medical notes.

TABLE 9A Demographic Data for Live Bipolar Cohort (n = 42) Frequency Frequency Years Future Future of Future of Future SubjectID- SAT1 since Hosp. w/o Hosp. due Hosp. w/o Hosp. due Visit Diagnosis Age Gender Ethnicity Levels testing suicidality to suicidality suicidality to suicidality phchp234v1 Bipolar II 44 M Caucasian 1955.2 0.83 0 0 0 0 Disorder phchp053v2 Bipolar I 58 M Caucasian 2178.3 5.67 4 0 0.71 0 Disorder phchp152v1 Bipolar I 45 M Caucasian 2178.8 2.33 0 0 0 0 Disorder phchp122v1 Bipolar Disorder 51 M Caucasian 2245.6 0.58 0 0 0 0 NOS phchp190v3 Bipolar Disorder 50 M Caucasian 2300.6 1.25 0 0 0 0 NOS phchp020v3 Bipolar Disorder 63 M Caucasian 2342.6 4.08 0 0 0 0 NOS phchp113v1 Bipolar I 37 M Caucasian 2437.4 3.00 0 0 0 0 Disorder phchp132v2 Bipolar I 51 M Caucasian 2558.9 2.33 0 0 0 0 Disorder phchp184v3 Bipolar Disorder 64 M Caucasian 2575.4 1.33 0 0 0 0 NOS phchp039v3 Bipolar I 52 M Caucasian 2580.1 5.75 0 0 0 0 Disorder phchp147v1 Bipolar II 38 M Caucasian 2582.8 2.25 0 0 0 0 Disorder phchp178v1 Bipolar I 49 M Caucasian 2616.8 1.0 0 0 0 0 Disorder phchp136v3 Bipolar I 41 M Caucasian 2635.9 2.0 0 0 0 0 Disorder phchp045v1 Bipolar I 36 M Caucasian 2721.0 5.42 0 0 0 0 Disorder phchp224v1 Bipolar I 59 M Caucasian 2748.1 1.08 1 1 0.92 0.92 Disorder phchp183v1 Bipolar I 48 M Caucasian 2750.9 0.42 2 1 4.80 2.40 Disorder phchp171v2 Bipolar Disorder 36 M Caucasian 2795.7 1.50 0 0 0 0 NOS phchp166v1 Bipolar Disorder 56 M Caucasian 2829.6 1.92 0 0 0 0 NOS phchp253v1 Bipolar Disorder 25 M Caucasian 2888.5 1.0 0 0 0 0 NOS phchp186v1 Bipolar II 43 M Caucasian 2901.5 1.67 0 0 0 0 Disorder phchp079v2 Bipolar Disorder 44 M Caucasian 3053.2 4.50 0 0 0 0 phchp128v1 Bipolar I 45 M Caucasian 3118.6 2.67 0 0 0 0 Disorder phchp080v1 Bipolar I 44 M Caucasian 3153.6 5.00 0 0 0 0 Disorder phchp088v1 Bipolar I 44 M Caucasian 3194.1 4.58 0 10 0 2.18 Disorder phchp109v1 Bipolar I 22 M Caucasian 3200.8 3.00 1 2 0.33 0.67 Disorder phchp134v3 Bipolar II 59 M Caucasian 3202.3 1.92 0 0 0 0 Disorder phchp153v1 Bipolar II 55 M Caucasian 3304.9 2.0 0 0 0 0 Disorder phchp274v2 Bipolar Disorder 48 M Caucasian 3349.0 0.50 0 0 0 0 NOS phchp140v3 Bipolar II 38 M Caucasian 3393.8 1.92 0 0 0 0 Disorder phchp030v3 Bipolar I 49 M Caucasian 3395.2 5.92 0 3 0 0.51 Disorder phchp124v1 Bipolar I 53 M Caucasian 3660.9 2.50 0 6 0 2.40 Disorder phchp095v3 Bipolar I 29 M Caucasian 3695.4 0.33 0 1 0 3.00 Disorder phchp100v1 Bipolar I 28 M Caucasian 3767.8 1.58 0 0 0 0 Disorder phchp210v3 Bipolar I 44 M Caucasian 3844.6 0.50 0 0 0 0 Disorder phchp219v1 Bipolar Disorder 61 M Caucasian 3845.1 1.17 0 0 0 0 NOS phchp031v3 Bipolar I 52 M Caucasian 4080.7 4.08 1 0 0.24 0 Disorder phchp093v3 Bipolar I 52 M Caucasian 4137.4 2.67 0 1 0 0.38 Disorder phchp067v1 Bipolar II 39 M Caucasian 4214.7 5.58 0 0 0 0 Disorder phchp142v3 Bipolar I 55 M Caucasian 4310.7 1.92 0 0 0 0 Disorder phchp112v2 Bipolar I 46 M Caucasian 4410.4 1.33 0 0 0 0 Disorder phchp149v2 Bipolar Disorder 45 M Caucasian 4586.9 2.00 1 0 0.5 0 NOS phchp117v1 Bipolar I 43 M Caucasian 6531.1 3.00 0 0 0 0 Disorder

TABLE 9B Demographic Data for Live Psychosis Cohort (n = 46) Frequency Frequency Years Future Future of Future of Future SubjectID- SAT1 since Hosp. w/o Hosp. due Hosp. w/o Hosp. due Visit Diagnosis Age Gender Ethnicity Levels testing suicidality to suicidality suicidality to suicidality phchp222v2 Schizophrenia 60 M Caucasian 1410.6 0.67 0 0 0 0 phchp175v1 Schizoaffective 42 M Caucasian 1773.9 2.08 0 0 0 0 Disorder phchp139v1 Schizophrenia 24 M Caucasian 1774.6 0.25 0 0 0 0 phchp025v1 Schizophrenia 42 M Caucasian 2004.6 6.83 0 0 0 0 phchp051v1 Schizoaffective 52 M Caucasian 2083.8 5.83 0 0 0 0 Disorder phchp148v1 Schizophrenia 25 M Caucasian 2254.7 2.17 1 0 0.46 0 phchp133v1 Schizophrenia 55 M Caucasian 2286 2.75 0 2 0 0.73 phchp033v1 Schizoaffective 48 M Caucasian 2291.4 2.58 0 1 0 0.39 Disorder phchp027v1 Schizoaffective 40 M Caucasian 2406.3 6.67 3 0 0.45 0 Disorder phchp012v1 Schizoaffective 55 M Caucasian 2458.1 5.17 1 1 0.19 0.19 Disorder phchp089v2 Schizoaffective 33 M Caucasian 2545.3 4.42 0 0 0 0 Disorder phchp060v1 Schizophrenia 62 M Caucasian 2589.2 3.50 2 0 0.57 0 phchp046v1 Schizoaffective 45 M Caucasian 2732.3 6.17 0 1 0 0.16 Disorder phchp103v1 Schizoaffective 61 M Caucasian 2763.7 2.58 1 2 0.39 0.77 Disorder phchp010v2 Schizoaffective 45 M Caucasian 2778.5 6.92 0 0 0 0 Disorder phchp005v1 Schizoaffective 45 M Caucasian 2797.8 7.33 1 1 0.14 0.14 Disorder phchp022v1 Schizophrenia 48 M Caucasian 2846.6 6.83 0 0 0 0 phchp195v3 Schizophrenia 53 M Caucasian 2846.6 1.17 0 0 0 0 phchp129v1 Schizoaffective 22 M Caucasian 2871.5 2.83 5 1 1.76 0.35 Disorder phchp120v1 Delusional 51 M Caucasian 2877.9 3.00 0 0 0 0 Disorder phchp211v1 Schizophrenia 62 M Caucasian 2879.9 1.25 0 0 0 0 phchp277v2 Schizophrenia 50 M Caucasian 2904.8 0.58 0 0 0 0 phchp101v1 Schizoaffective 74 M Caucasian 2923.7 3.67 0 1 0 0.27 Disorder phchp116v1 Schizoaffective 47 M Caucasian 2962.1 0.50 0 1 0 2.00 Disorder phchp052v1 Schizophrenia 60 M Caucasian 2989.9 0.83 0 0 0 0 phchp090v3 Schizophrenia 24 M Caucasian 3046.4 1.00 0 2 0 2.00 phchp197v1 Schizophrenia 56 M Caucasian 3046.6 1.67 1 0 0.60 0 phchp061v3 Schizophrenia 50 M Caucasian 3115.6 4.92 1 6 0.20 1.22 phchp057v1 Schizoaffective 47 M Caucasian 3233.8 5.92 0 0 0 0 Disorder phchp105v2 Schizoaffective 59 M Caucasian 3297.6 2.83 2 0 0.71 0 Disoder per chip phchp087v3 Schizoaffective 66 M Caucasian 3523.5 4.25 0 0 0 0 Disorder phchp091v1 Schizoaffective 55 M Caucasian 3534.5 4.75 0 0 0 0 Disorder phchp069v3 Schizophrenia 48 M Caucasian 3819.8 5.25 0 0 0 0 phchp062v3 Schizophrenia 57 M Caucasian 3878.8 5.42 0 0 0 0 phchp099v2 Schizophrenia 49 M Caucasian 3993.4 3.58 0 0 0 0 phchp049v1 Schizoaffective 46 M Caucasian 4012.3 6.08 0 0 0 0 Disorder phchp040v3 Schizoaffective 50 M Caucasian 4019.2 5.25 1 0 0.19 0 Disorder phchp042v3 Schizoaffective 44 M Caucasian 4124.5 5.50 0 0 0 0 Disorder phchp075v3 Schizoaffective 58 M Caucasian 4127.1 4.83 1 5 0.21 1.03 Disorder phchp108v2 Schizophrenia 42 M Caucasian 4231.9 3.17 0 0 0 0 phchp085v3 Schizoaffective 57 M Caucasian 4335.9 4.50 0 0 0 0 Disorder phchp151v3 Schizophrenia 24 M Caucasian 4390.9 2.00 1 1 0.50 0.50 phchp065v3 Schizoaffective 62 M Caucasian 4439.2 5.25 0 0 0 0 Disorder phchp086v3 Schizophrenia 49 M Caucasian 4545.4 4.25 0 0 0 0 phchp073v3 Schizoaffective 65 M Caucasian 4874.4 4.92 0 12 0 2.44 Disorder phchp072v3 Schizoaffective 60 M Caucasian 5911.1 5.08 0 1 0 0.20 Disorder

For future hospitalization analyses, robust multi-array analysis (RMA) as described in Example 1 was conducted and normalized for each cohort, prior to looking at biomarker levels in individual subjects. One-tail t-tests with equal variance were used for statistical comparisons. ROC curves were calculated using SPSS software for each of the four-dimensional analyses, predicting the state variable of hospitalizations due to suicidality.

Higher SAT1 levels compared to lower SAT1 levels at time of testing differentiated future as well as past hospitalizations due to suicidality in the bipolar disorder subjects (FIGS. 5A-5E). A similar, but weaker, pattern was exhibited in the psychosis (schizophrenia/schizoaffective) subjects (FIGS. 6A-6E). Remarkably, besides SAT1, three other biomarkers (PTEN, MARCKS and MAP3K3) of the six biomarkers that survived Bonferroni correction in the suicide completers cohort validation step also showed similar but weaker results (Table 10 and FIGS. 7A-7C).

TABLE 10 Prospective and Retrospective Differentiation of Hospitalizations and Suicidality Psychosis (n = 46) Bipolar Disorder (n = 42) Schizophrenia/Schizoaffective Future Past Future Past Hospitalizations Hospitalizations Hospitalizations Hospitalizations (since testing) (prior to testing) (since testing) (prior to testing) With With With With Without Suicidality Without Suicidality Without Suicidality Without Suicidality SAT1 NS H: 0.1195 NS H: 0.0743 NS NS NS H: 0.0274 T: 0.0484 T: 0.0363 (H: 0.0519) T: 0.0742 PTEN NS H: 0.0271 NS H: 0.0598 NS NS NS NS T: 0.0324 T: 0.0491 MARCKS NS NS NS H: 0.0227 NS NS NS NS T: 0.0242 MAP3K3 NS NS NS H: 0.2052 NS NS NS NS T: 0.0273 UBA6 NS NS NS NS NS NS NS NS MT-ND6 NS NS NS NS NS NS NS NS Panel of 3 NS H: 0.0184 NS H: 0.04905 NS NS NS NS (SAT1, PTEN, T: 0.0530 T: 0.04914 MAP3K3) Panel of 6 NS H: 0.1501 NS H: 0.0728 NS NS NS NS (SAT1, PTEN, T: 0.0159 T: 0.0101 MAP3K3, UBA6, MARCK, MT-ND6)

Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 may be not only a state marker but perhaps a trait marker as well.

A multi-dimensional approach was also conducted to predict future hospitalizations, by adding data about mood, anxiety, and psychosis to the data about SAT1 expression levels (FIGS. 8A-8C). The ROC curve improved in a step-wise fashion, from an AUC of 0.640 with SAT1 alone, to an AUC of 0.798 with SAT1 and anxiety, an AUC of 0.813 with SAT1, anxiety and mood, and an AUC of 0.835 with SAT1, anxiety, mood and psychosis. Levels of SAT1 were identified that provided different levels of sensitivity and specificity (Table 11). The anxiety and mood information was obtained from simple visual analog scales, previously described in Niculescu, et al., “PhenoChipping of psychotic disorders: a novel approach for deconstructing and quantitating psychiatric phenotypes. American Journal of Medical Genetics. Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics 141B, 653-662, doi:10.1002/ajmg.b.30404 (2006).

TABLE 11 SAT1 Expression Level Cut-offs from the ROC Curve (FIGS. 8A-8C) SAT1 Expression Cut-off Levels Sensitivity Specificity Accuracy Higher 2723.512 100.00% 41.18% 70.59% Sensitivity Intermediate 3173.874  75.00% 61.76% 68.38% Higher 3394.539  50.00% 73.53% 61.77% Specificity

The multi-dimensional approach described above for SAT1 was also conducted to predict future hospitalizations, by adding data about mood, anxiety, and psychosis to the data about the six top biomarkers' expression levels (BioM 6, including the biomarkers SAT1, PTEN, MARCKS, MAP3K3, UBA6, and MT-ND6; FIGS. 9A-9B).

These results demonstrate that combining clinical scale data for anxiety and mood with the blood biomarker date improves predictability of increased suicide ideation and/or future hospitalization.

The psychosis information was based on combining of the scores on the hallucinations and delusions in the PANSS (FIG. 10). Of note, this simple clinical-genomic approach did not directly ask about suicidal ideation, which some individuals may deny or choose not to share with clinicians.

Using discovery in live subjects and validation in suicide completers, possible biomarkers for suicidality were found. The top biomarker finding, SAT1, as well as PTEN, MARCKS and MAP3K3, were additionally validated by prospective and retrospective analyses in live subjects, looking at ability to predict and differentiate future and past hospitalizations due to suicidality in bipolar disorder and psychosis (schizophrenia/schizoaffective) (Table 10).

Beyond predictions, as a window into the biology of suicidality, the current Examples show overlap at a gene and pathway level with apoptosis. SAT1, for example, is a key catabolic enzyme for polyamines Polyamine levels within cells control cell viability, and significant decreases in polyamine levels can result in apoptosis. They seem to reflect an endowment for cellular and organismal activity and growth, key characteristics of mood. SAT1, which is increased in live suicidal ideation subjects and in suicide completers in the Examples, is highly inducible by a variety of stimuli, including toxins, cytokines, heat shock, ischemia, and other stresses. SAT1 overexpressing mice had alterations in their polyamine pool, hair loss, infertility and weight loss (Pietila et al., Activation of polyamine catabolism profoundly alters tissue polyamine pools and affects hair growth and female fertility in transgenic mice overexpressing spermidine/spermine N1-acetyltransferase. J Biol. Chem. 272, 18746-18751 (1997); Min et al., Altered levels of growth-related and novel gene transcripts in reproductive and other tissues of female mice overexpressing spermidien/spermine N1-actyltransferase (SSAT). J. Biol. Chem. 277, 3647-3657, doi:10.1074/jbc.M100751200 (2002)). Turecki and colleagues have provided compelling evidence for changes in the polyamine system in the brain of suicide completers (Fiori et al., Global gene expression profiling of the polyamine system in suicide completers. Int. J. Neuropsychopharmacol. 14, 595-605, doi:10.1017/S1461145710001574 (2011)).

CD24, the top biomarker found to decrease in suicidal subjects, also has roles in apoptosis. Specifically, mice lacking CD24 showed an increased rate of apoptosis (Duckworth et al. CD24 is expressed in gastric parietal cells and regulates apoptosis and the response to Helicobacter felis infection in the murine stomach. American Journal of Physiology. Gastrointestinal and liver physiology 303, G915-926, doi:10.1152/ajpgi.00068.2012 (2012)).

It could be that simpler mechanisms related to cellular survival and programed cell-death decision have been recruited by evolution for higher mental functions such as feelings, thoughts, actions and behaviors leading to suicidality. In that sense, suicidality could be viewed as whole-organism self-apoptosis. Apoptosis mechanisms have previously been implicated in mood disorders, and their inhibition in affective resilience (Malkesman et al. Targeting the BH3-interacting domain death agonist to develop mechanistically unique antidepressants. Mol. Psychiatry 17, 770-780, doi:10.1038/mp.2011.77 (2012)). Interestingly, lithium, a medication with clinical evidence for preventing suicidality in bipolar disorder, has anti-apoptotic effects at a cellular level (Lowthert et al., Increased ratio of anti-apoptotic to pro-apoptotic BcI2 gene-family members in lithium-responders one month after treatment initiation. Biology of Mood & Anxiety Disorders 2, 15, doi:10.1186/2045-5380-2-15 (2012)). Imaging studies have shown reduced gray matter volume in the brain of individuals with bipolar disorder and history of suicide attempts. Long-term lithium treatment was associated with increased gray matter volumes in the same areas where suicide was associated with decreased gray matter (Benedetti et al., Opposite effects of suicidality and lithium on gray matter volumes in bipolar depression. J Affect Disord 135, 139-147, doi:10.1016/j.jad.2011.07.006 (2011)).

In view of the above, it will be seen that the several advantages of the disclosure are achieved and other advantageous results attained. As various changes could be made in the above methods without departing from the scope of the disclosure, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

When introducing elements of the present disclosure or the various versions, embodiment(s) or aspects thereof, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. 

What is claimed is:
 1. A method for assessing and mitigating suicidality in a male bipolar subject in need thereof, comprising: determining an expression level of at least a first panel of blood biomarkers or a second panel of blood biomarkers in a sample from the subject, where the first panel of blood biomarkers comprises small cell lung carcinoma cluster 4 antigen (CD24; CD24 molecule), ATPase type 13A2 (ATP13A2), epoxide hydrolase 1, microsomal (xenobiotic) (EPHXl), HtrA serine peptidase 1 (HTRAl); leptin receptor (LEPR), spectrin beta non-erythrocytic 1 (SPTBNl), muscleblind-like 2 (MBNL2), olfactory receptor family 2 subfamily J member 3 (OR2J3), Ras homolog enriched in brain (RHEB), and where the second panel of blood biomarkers comprises spermidine/spermine Nl-acetyltransferase 1 (SATl); forkhead box N3 (FOXN3), guanylate binding protein 1 (GBPl), phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5), apolipoprotein L2 (APOL2), ATPase H+ transporting lysosomal 9 kDa, VO subunit el (ATP6V0E1), GRINLlA complex locus (GCOMl), interleukin 1 beta (ILlB), lipoma HMGIC fusion partner (LHFP), lipase A (LIPA), myristoylated alanine-rich protein kinase C substrate (MARCKS), 6-phosphogluconolactonase (PGLS), phosphatase and tensin homolog (PTEN), reversion-inducing-cysteine-rich protein with kazal motifs (RECK), tumor necrosis factor (ligand) superfamily member 10 (TNFSFlO), ATP-binding cassette, subfamily A (ABCl) member 1 (ABCAl), Rho guanine nucleotide exchange factor (GEF) 40 (ARHGEF4; FLJ10357), cancer susceptibility candidate 1 (CASCl), dehydrogenase/reductase (SDR family) member 9 (DHRS9), disrupted in schizophrenia 1 (DISCl), eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2), uncharacterized LOC727820 (LOC727820), mitogen-activated protein kinase kinase kinase 3 (MAP3K3), mitochondrially encoded NADH dehydrogenase 6 (MT-ND6; ND6), RNA binding motif protein 47 (RBM47), RPTOR independent companion of MTOR complex 2 (RICTOR), sterile alpha motif domain containing 9-like (SAMD9L), scavenger receptor class F member 1 (SCARFl), solute carrier family 36 (proton/amino acid symporter) member 1 (SLC36Al), signal transducer and activator of transcription 1, 91 kDa (STATl), cytochrome c oxidase subunit Vb (COX5B), SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 1 (SMARCAl), ubiquitin-like modifier activating enzyme 6 (UBA6), zinc finger CCCH-type antiviral 1 (ZC3HAV1) and tyrosine kinase, non-receptor 2 (TNK2); identifying a subject having suicidality where the expression level of the blood biomarkers in the first panel is decreased relative to a reference expression level, or, the expression level of the blood biomarkers in the second panel is increased relative to a reference expression level; and, administering to the subject identified as having suicidality a drug to treat the suicidality.
 2. The method according to claim 1, where the identifying step further comprises comparing a biomarker panel score of the subject to a biomarker panel score of a reference.
 3. The method according to claim 1, wherein: (a) individuals who have changes in one or more of SPTBN1, MBNL2, S100A8 are treated with clozapine; and (b) individuals who have changes in one or more of SAT1, GBP1, IL1B, LHFP, MAP3K3, S100A8 are treated with docosahexaenoic acid (DHA). 